JPH0556863A - Cooker - Google Patents

Abstract

PURPOSE:To improve the finish state of cooking by estimating the surface and rear face temperatures of food under cooking in real time through the environmental physical quantity in a heating compartment that can actually be measured and detected. CONSTITUTION:An environmental physical quantity detector 5 detects the atmospheric temperature in a heating compartment 1. A voltage level detector 6 detects the voltage level of a commercial power source, and a timer 7 counts elapsed the time period since the power source is turned on. A temperature estimating device 11 estimates the surface and rear face temperatures of food under cooking based on the outputs from the environmental physical quantity detector 5, voltage level detector 6, timer 7 and a category selecting key 9. A controller 12 controls a first heater 13 and a second heater 14, that are respectively mounted at the upper and lower parts in the heating compartment 1, based on the outputs from the temperature estimating device 11. Thereby, the finish state of cooking can be improved.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a cooking utensil for automatically cooking in a microwave oven, a gas oven, a roaster or the like.

[0002]

2. Description of the Related Art Conventionally, a cooking utensil of this type, for example, an electric roaster has been constructed as shown in FIG. The configuration will be described below.

As shown in the figure, a heating chamber 1 is for cooking food by putting it in it. A heating supply means (heater) 2 for heating the food is provided and a temperature detecting means such as a thermistor for detecting the internal temperature. 3 is provided. The control means 4 controls the heating supply means 2 based on the information from the temperature detection means 3. In order to automatically cook with such a configuration, it is necessary to know the weight of the food and the initial temperature. Therefore, the output voltage gradient of the temperature detecting means 3 is measured for several minutes after the power is turned on, and if the gradient is steep, it is determined that the weight of the food is light, and if the gradient is gentle, the weight is heavy, and the voltage gradient is determined. The optimum cooking time was determined by multiplying the constant K by The output voltage characteristic of the temperature detecting means 3 is shown in FIG. Figure 1
3 (a) has a light weight, and FIG. 13 (b) has a heavy weight. And I did a lot of cooking experiments and decided the constant.

[0004]

In such a conventional cooking utensil (here, a roaster), the gradient of the ambient temperature in the heating chamber 1 is detected, and the cooking product (for example, fish grill) is detected based on the gradient. The cooking time was decided by judging the weight of), so there was considerable variation in the completion of cooking. For example, the initial temperature in the heating chamber 1 is not always low, and when cooking is performed immediately after finishing the cooking, the initial temperature in the heating chamber 1 becomes very high. In this case, when a heavy food is cooked, the output voltage characteristic of the temperature detecting means 3 is as shown in FIG. 14, and the temperature in the heating chamber 1 drops for a moment. This is because even if cooking is started, the temperature in the heating chamber 1 is high, and thus the heat in the heating chamber 1 is absorbed by the food. In such a case, it was difficult to determine the optimum cooking time by the above method. Also, the heating supply means 2
Is a heater, it has a considerable effect on the completion of cooking due to fluctuations in commercial power supply voltage. In other words, the environment at the time of starting cooking (type of food, initial temperature of heating chamber 1,
There was a problem that the finished cooking varied considerably depending on the initial temperature of the cooked food, the power supply voltage, etc.). Furthermore, with regard to the baking of fish etc., it is very difficult to detect the completion of cooking because it is necessary to consider the baking state of the front side and the baking state of the back side. Was there.

The present invention is to solve the above-mentioned problems, and to improve the finished state of cooking by estimating the real time with the environmental physical quantity in the heating chamber that can actually measure and detect the surface temperature and the backside temperature of the food. Has a purpose.

[0006]

In order to achieve the above object, the present invention provides a heating chamber for storing food for cooking, and first and second heating supply means for heating the food. Environmental physical quantity detection means for detecting the environment in the heating chamber, temperature estimation means for estimating the surface temperature and the back surface temperature of the cooking object based on the output of the environmental physical quantity detection means, and the first based on the output of the temperature estimation means A control means for controlling the first and second heating supply means, wherein the control means is
Only the first heating and supplying means is heated until the back surface temperature output of the temperature estimating means reaches a predetermined value T1, and thereafter only the second heating and supplying means is heated, and the surface temperature of the temperature estimating means is increased. When the output reaches a predetermined value T2, the heating of the first and second heating supply means is stopped, and the temperature estimation means obtains the cooking obtained by a method simulating a neural network composed of a plurality of neural elements. An object of the present invention is to provide a hierarchical neural network schematic means having a plurality of connection weight coefficients of a fixed neural network for estimating the temperature of an object therein.

[0007]

According to the present invention, by the above-mentioned means for solving the problems, by inputting the environmental information in the heating chamber from the environmental physical quantity detecting means to the temperature estimating means, all the actual cooking environments to be actually cooked are learned and fixed inside. The temperature estimation means having the neural network model means having the combined weighting coefficient is used to estimate the surface temperature and the backside temperature of the cooking product moment by moment. The control means is the first heating supply means (heater in the electric roaster) until the back surface temperature output of the temperature estimation means reaches the predetermined value T1.
Then, the second heating and supplying means is heated, and when the surface temperature reaches the predetermined value T2, the heating of the first and second heating and supplying means is stopped. By indirectly detecting the temperature rise of the food on the back and front, it is possible to recognize the optimal completion of cooking.

[0008]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An electric roaster according to an embodiment of the present invention will be described below with reference to FIGS. In addition,
The same components as those of the conventional example are designated by the same reference numerals and the description thereof will be omitted.

As shown in the figure, the environmental physical quantity detection means 5
Is for detecting the environment inside the heating chamber 1, and in the present embodiment, is constituted by a thermistor or the like to detect the ambient temperature inside the heating chamber 1. The voltage level detecting means 6 detects the voltage level of the commercial power supply voltage. The time counting means 7 counts the time elapsed since the power was turned on. The operation means 8 is a category selection key 9 for selecting a category of food and cooking start /
It is composed of a cooking key 10 for stopping. The temperature estimating means 11 estimates the surface temperature and the back surface temperature of the cooked food based on the outputs of the environmental physical quantity detecting means 5, the voltage level detecting means 6, the time measuring means 7 and the category selection key 9, and the control means 12 The first heating supply means 13 and the second heating supply means 14 are controlled based on the output of the temperature estimation means 11. The first heating supply means 13 and the second heating supply means 14 are heaters. The first heating and supplying means 13 is arranged in the lower part of the heating chamber 1, and the second heating and supplying means 1 is provided.
Reference numeral 4 is provided above the heating chamber 1. The display means 15 comprises a fluorescent display tube and displays the remaining cooking time and the like. The A / D conversion means 16 and 17 are for converting the outputs of the environmental physical quantity detection means 5 and the voltage level detection means 6 into digital quantities, respectively. Operation means 8 and display means 15
Is configured as shown in FIG.

Since the solving means used in the conventional control method cannot be applied to the means constituting the temperature estimating means 11, it is constructed by a method simulating an optimal neural network as a multidimensional information processing method. There is. In the method simulating a neural network, a method of using a plurality of coupling weight coefficients of the neural network for estimating the temperature (front surface temperature, back surface temperature) of a cooked food as a fixed table, and leaving the learning function and using the environment and There is a way to adapt to the person. The present embodiment is provided with a temperature estimating means 11 having a neural network model means having therein a fixed coupling weighting coefficient for estimating the temperature of a food item obtained by a method simulating a neural network.

Factors that affect the completion of the cooked food include the initial temperature in the heating chamber, the initial temperature of the cooked food, the type (category) of the cooked food, and the voltage level of the commercial power supply. Due to these factors, the completion of the cooked food will vary greatly.

The coupling weight coefficient fixed in the neural network for estimating the temperature of the cooked food is the surface temperature of the cooked food when cooked in the actual cooking environment (an environment in which various factors described above are combined). Data on how the backside temperature and the ambient temperature inside the heating chamber change is collected, and the correlation between the environmental data, the ambient temperature data inside the heating chamber, and the temperature (front side and backside) of the cooked food is correlated to the neural network model means. Can be obtained by learning.
As a neural network schematic means to be used, reference 1 (D.
E. Ramelhart et al. 2 authors, translated by Shunichi Amari "PDP model" 1989), Reference 2 (Kaoru Nakano and 7 others "Basics of Neurocomputers", Corona Publishing Co., P102, 1)
990) and Japanese Patent Publication No. 63-55106. Hereinafter, the configuration and operation of a concrete neural network schematic means will be described by taking a multilayer perceptron using an error backpropagation method as the most well-known learning algorithm described in Document 1 as an example.

FIG. 3 is a conceptual diagram of a neural element which is a constituent unit of the neural network model means. In FIG. 3, 21 to 2
N is a pseudo synapse coupling converter simulating synaptic coupling of nerves, and 2a is pseudo synapse coupling converters 21 to 2N.
2b is a set nonlinear function, for example, a sigmoid function whose threshold is h, f (y, h) = 1 / (1 + exp (-y + h)) (Equation 1 ) Is a non-linear converter for non-linearly converting the output of the adder 2a. Although omitted because the drawing is complicated, an input line for receiving a correction signal from the correction means is connected to the pseudo synapse coupling converters 21 to 2N and the non-linear converter 2b. Further, the pseudo synapse coupling converters 21 to 2N serve as coupling weight coefficients of the neural network schematic means. This neural element has two types of operation modes, a signal processing mode and a learning mode.

The operation of each mode of the neural element will be described below with reference to FIG. First, the operation of the signal processing mode will be described. Neural elements are N inputs X1 to X
It receives n and outputs one output. i-th side input signal Xi
Is the i-th pseudo-synaptic coupling converter 2 shown by a square
i is converted to Wi · Xi. N signals W1 · X1 converted by the pseudo synapse coupling converters 21 to 2N
Wn · Xn enters the adder 2a, the addition result y is sent to the nonlinear converter 2b, and becomes the final output f (y, h). Next, the operation of the learning mode will be described. In the learning mode, the conversion parameters W1 to Wn and h of the pseudo synapse coupling converters 21 to 2N and the non-linear converter 2b are received, and the correction signals representing the correction parameter correction amounts ΔW1 to ΔWn and Δh are received from the correction means. , Wi + ΔWi; i = 1, 2, ..., N h + Δh (formula 2).

FIG. 4 is a conceptual diagram of a signal converting means constituted by connecting four neural elements in parallel. Needless to say, the following description does not specify that the number of neural elements constituting this signal converting means is four. In FIG. 4, 211 to 244 are pseudo synapse coupling converters,
201 to 204 are addition nonlinear converters that combine the adder 2a and the nonlinear converter 2b described in FIG. 4, the illustration is omitted because the drawing is complicated as in FIG. 3, but the input lines for receiving the correction signal from the correction means are pseudo synapse coupling converters 211 to 244 and an addition nonlinear converter 20.
It is connected to 1-204. The pseudo synapse coupling converters 211 to 244 also serve as coupling weight coefficients. Regarding the operation of this signal conversion means, the operation of the neural element described in FIG. 3 is performed in parallel.

FIG. 5 is a block diagram showing the configuration of the signal processing means when the error back-propagation method is adopted as the learning algorithm, and 31 is the above-mentioned signal converting means. However,
Here, M neural elements for receiving N inputs are arranged in parallel. Reference numeral 32 is a correction means for calculating the correction amount of the signal conversion means 31 in the learning mode. Hereinafter, the operation when learning the signal processing means will be described with reference to FIG. The signal converting means 31 has N inputs S
Upon receiving _in (X), it outputs M outputs S _out (X).
The correction means 32 includes an input signal S _in (X) and an output signal S _out.
Upon receiving (X), the process waits until M error signals δ _i (X) are input from the error calculating means or the signal converting means in the subsequent stage. The error signal δ _i (X) is input and the correction amount is set to ΔW _ij = δ _i (X) · S _iout (X) · (1−S _iout (X)) · S _jin (X) (i = 1 to N, j = 1-M) (Equation 3) is calculated and a correction signal is sent to the signal conversion means 31. The signal conversion means 31 modifies the conversion parameters of the internal neural elements according to the learning mode described above.

FIG. 6 is a block diagram showing the structure of a multilayer perceptron using a neural circuit retina type means.
X, 31Y, and 31Z are signal conversion means composed of K, L, and M neural elements, respectively, and are 32X, 32Y, and 3X.
2Z is a correction means, and 33 is an error calculation means. The operation of the multi-layer perceptron configured as described above will be described with reference to FIG. Signal processing means 3
In 4X, the signal conversion means 31X receives the input S
_{Receives iin} (X) (i = 1 to N) and outputs S _jout (X)
(J = 1 to K) is output. The correction means 32X uses the signal S
_The error signal δ is received by receiving _iin (X) and the signal S _jout (X).
Wait until _j (X) (j = 1 to K) is input. The same processing is performed in the signal processing means 34Y and 34Z, and the final output _Shout (Z) from the signal converting means 31Z.
(H = 1 to M) is output. Final output _Shout (Z)
Is also sent to the error calculation means 33. Error calculation means 33
, The error with the ideal output T (T1, ..., TM) is calculated based on the squared error evaluation function COST (Equation 4), and the error signal δ _h (Z) is corrected by the correction means. 32Z
Sent to.

[0018]

[Equation 1]

However, η is a parameter that determines the learning speed of the multilayer perceptron. Next, when the evaluation function is a square error, the error signal is δh (Z) = − η · (S _hout (Z) −T _h ) (Equation 5). The correction unit 32Z calculates the correction amount ΔW (Z) of the conversion parameter of the signal conversion unit 31Z according to the procedure described above, calculates the error signal to be sent to the correction unit 32Y based on (Equation 6), and the correction signal ΔW (Z) is sent to the signal converting means 31Z, and the error signal δ (Y) is corrected by the correcting means 32.
Send to Y. The signal conversion means 31Z uses the correction signal ΔW (Z).
Modify internal parameters based on. The error signal δ (Y) is given by (Equation 6).

[0020]

[Equation 2]

[0021] Here, W _ij (Z) signal converting means 31Z
Is a conversion parameter of the pseudo synapse coupling converter of. Hereinafter, similar processing is performed in the signal processing means 34X and 34Y. By repeating the following procedure called learning, the multi-layer perceptron produces an output that closely approximates the ideal output T when given an input. In the above description, the three-stage multi-layer perceptron is used, but this may be any number of stages. In addition, reference 1
The modification method of the conversion parameter h of the non-linear conversion means in the signal conversion means and the method of accelerating learning known as the inertia term are omitted for simplification of the description, but this omission is described below. It does not bind the invention.

In this way, the neural network model means uses the environmental data (the initial temperature in the heating chamber, the initial temperature of the cooking product, the commercial power supply voltage level, the type of the cooking product, etc.) when cooking and the ambient temperature data in the heating chamber. By learning the relationship with the temperature (front surface temperature, back surface temperature) data of the food, it is possible to express in a natural way the control method that is not easy to describe with simple rules. In this embodiment, the temperature estimation means 11 is configured by incorporating the information thus obtained. Specifically, the temperature estimating means 11 is configured by using only the signal converting means 31X, 31Y, 31Z of the multilayer perceptron after sufficiently learning as a neural network schematic means. The data actually learned will be described.

FIG. 7 shows the characteristics when cooking is performed when the initial temperature of the heating chamber 1 is low, the commercial power supply voltage is 100 V, the type of food is one horse mackerel, and the initial temperature of the food is about 10 ° C. It is a thing. FIG. 7A shows a change in the environmental physical quantity detection means 5 (ambient temperature in the heating chamber 1), FIG. 7B shows a surface temperature of the cooked product, and FIG. 7C shows a change in backside temperature of the cooked product. Is shown. The temperature of the cooked food is measured with a thermocouple or the like. FIG. 8 shows characteristics when cooking is performed when the initial temperature of the heating chamber 1 is high, the commercial power supply voltage is 100 V, the type of food is one horse mackerel, and the initial temperature of the food is approximately 10 ° C. .. FIG. 9 shows the characteristics when cooking is performed when the initial temperature of the heating chamber 1 is low, the commercial power supply voltage is 100 V, the type of food is 4 horse mackerels, and the initial temperature of the food is approximately 10 ° C. is there. FIG. 10 shows the characteristics when cooking is performed when the initial temperature of the heating chamber 1 is high, the commercial power supply voltage is 100 V, the type of food is 4 horse mackerels, and the initial temperature of the food is approximately 10 ° C. is there.

FIGS. 8 (a) to 8 (c), 9 (a) to 9 (c), 10 (a) to 10 (c) are shown in FIG. 7 (a).
~ Corresponds to Fig. 7 (c). It can be seen that the output voltage change (ambient temperature in the heating chamber 1) of the environmental physical quantity detection means 5 varies depending on the initial temperature in the heating chamber 1 and the amount of food. Similarly, when the power supply voltage is changed, or when the type of food is changed, the output changes differently. Such an experiment was carried out in the same manner for all combinations of actual cooking environments. Then, the experimental data was input to the neural network model means for learning. That is, the heating chamber 1 of the environmental physical quantity detection means 5 is connected to the neural network schematic means.
Atmosphere temperature information inside, atmosphere temperature information 1 minute before the present time as atmosphere temperature gradient information, commercial power supply voltage level information of the voltage level detection means 6, and elapsed time information from the time of power-on obtained from the timekeeping means 7. And 5 pieces of category information obtained from the category selection key 9 and 2 pieces of information of the front surface temperature information and the back surface temperature information of the cooked food are input and learned, and the signal conversion means 31X in the neural network model means is input. , 31Y, 31Z are established, and they are incorporated in the surface temperature estimating means 11 as a neural network model means.

Next, the operation will be described based on the system configuration diagram shown in FIG. First, the food to be cooked is placed in the heating chamber 1, and the cooking category is selected by the category selection key 9 of the operation means 8. Then, cooking is started with the cooking key 10. The category information is input to the temperature estimation means 11 via the control means 12. The control means 12 outputs a signal to start timing to the timing means 7 and also outputs a heating start signal to cause the first heating supply means 13 to generate heat. The timing information of the timing means 7 is input to the temperature estimation means 11. Then, the environmental physical quantity information (atmosphere temperature information) in the heating chamber 1 is digitally converted from the output of the environmental physical quantity detecting means 5 by the A / D converting means 16 and input to the temperature estimating means 11 every moment. Further, the voltage level information of the commercial power supply voltage from the voltage level detecting means 6 is digitally converted by the A / D converting means 17 and input to the temperature estimating means 11. The temperature estimating means 11 estimates the front surface temperature and the back surface temperature of the cooking product momentarily based on these input signals and information, and outputs the information to the control means 12. The control means 12 firstly operates the first heating supply means 1 until the estimated back surface information reaches a predetermined value T1 (100 ° C. in this embodiment).
3 is controlled and heated, and then the second heating supply means 1
4 is controlled and heated, and when the estimated surface temperature reaches a predetermined value (110 ° C. in this embodiment), it is determined that the cooking is completed, and the first heating supply means 13 and the second heating supply means 14 are heated. To stop. FIG. 11 shows the control sequence. Further, in this embodiment, the predetermined values T1 and T2 are 10
Although 0 ° C. and 110 ° C. are set, this value does not restrict the present invention and can be changed depending on the type of food.

Further, the clocking means 7, the temperature estimating means 11, and the control means 12 are all constructed by a 4-bit microcomputer, but it is of course possible to construct them by one microcomputer. It should be noted that the temperature estimation means 11 includes temperature gradient information of the environmental physical quantity detection means 5 (two pieces of information at the present time and one minute ago), voltage level information of the commercial power supply voltage obtained from the voltage level detection means 6, and the time counting means 7. Five pieces of information, namely, elapsed time information after turning on the power source and category information of the cooking product obtained from the category selection key 9 are input, but this limitation does not restrict the present invention. Further, although the ambient temperature information is used as the environmental physical quantity information, it goes without saying that smoke information, burn color information, humidity information, vapor information, or the like, or a combination thereof can also be applied. Further, in the present embodiment, the electric roaster is used as the cooking utensil, but a microwave oven, a gas oven or the like may be used. Particularly, in the microwave oven, it is most suitable when the food is thawed. In other words, since the temperature of the cooked product can be estimated, it suffices to finish the cooking in the vicinity of the phase transition from solid (ice) to liquid (water) (around 0 ° C.). At this time, as the environmental physical quantity information, the ambient temperature may be used, and the absorption amount of electric waves of the cooking product (electric field strength)
The information is optimal.

As described above, according to the present embodiment, the temperature estimation incorporating the neural network model means having a plurality of fixed coupling weight coefficients of the neural network already learned in the environment of the heating chamber where the cooking is actually performed Since it is configured with means,
The finished state of the cooked food can be detected by the front surface temperature and the rear surface temperature, and at that temperature, the first heating and supplying means 13 arranged in the lower part and the second heating and supplying means arranged in the upper part of the heating chamber 1. Since 14 is controlled, the cooking state can be improved as compared with the conventional example, and optimum automatic cooking can be realized.

[0028]

As is apparent from the above embodiments, according to the present invention, there is provided a heating chamber for storing food to be cooked,
First and second heating supply means for heating the cooked food, an environmental physical quantity detection means for detecting the environment in the heating chamber, and a front surface temperature and a back surface temperature of the cooked food based on the output of the environmental physical quantity detection means. The temperature estimating means for estimating and the control means for controlling the first and second heating and supplying means based on the output of the temperature estimating means are provided, and the control means is such that the back surface temperature output of the temperature estimating means is a predetermined value. Only the first heating and supplying means is heated until reaching T1, then only the second heating and supplying means is heated, and when the surface temperature output of the temperature estimating means reaches the predetermined value T2, the first heating and supplying means is heated. Since the heating of the second heating supply means is stopped, it is possible to indirectly detect the actual backside temperature and the actual surface temperature of the cooked product that can recognize the finished state of the cooked product, and further, at that temperature, the heating chamber. Since the first heating supply means and the second heating supply means provided are controlled, the state of completion of cooking is higher than that in the case where the optimum cooking time is determined from the gradient such as the atmospheric temperature which has been conventionally used. Can be well.

The temperature estimating means estimates the temperature (front surface temperature, back surface temperature) of the cooked food obtained by a method simulating a neural network composed of a plurality of neural elements. Since a neural network model means having a plurality of coupling weight coefficients inside is provided, or a hierarchical neural network model means constructed by combining layers composed of a plurality of neural elements in multiple layers is provided, the heating chamber Initial temperature of
The temperature of the cooked food can be estimated regardless of the initial temperature of the cooked food, the amount of the cooked food, etc., and automatic cooking is possible.

Further, the environmental physical quantity detecting means includes a temperature detecting means for detecting the ambient temperature in the heating chamber, and the temperature detecting means indirectly estimates the temperature of the cooked food, so that the temperature of the cooked food is actually detected. It is not necessary to use a sensor (for example, a pyroelectric infrared sensor that measures the surface temperature of a food product in a non-contact manner or a temperature sensor that directly contacts the food product), and the cost can be reduced.

[Brief description of drawings]

FIG. 1 is a system configuration diagram of a cookware according to an embodiment of the present invention.

FIG. 2 is a front view of an operation unit and a display unit of the cooking utensil.

FIG. 3 is a conceptual diagram of a neural element that is a constituent unit of a neural network schematic means used in the cooking utensil.

FIG. 4 is a conceptual diagram of a signal conversion means composed of neural elements used in the cooking utensil.

FIG. 5 is a block diagram of a signal processing unit that employs an error backpropagation method as a learning algorithm used for the cooking utensil.

FIG. 6 is a block diagram showing a configuration of a multilayer perceptron using a neural network schematic means used in the cooking utensil.

FIG. 7 is a diagram showing an example of experimental data of the cooking utensils of (a) to (c).

FIG. 8 is a diagram showing another example of experimental data of the cooking utensils (a) to (c).

FIG. 9 is a diagram showing another example of experimental data of the cooking utensils (a) to (c).

FIG. 10 is a diagram showing another example of experimental data of (a) to (c) the cooking utensil.

FIG. 11 is a diagram showing a control sequence of the cooking appliance.

FIG. 12 is a system configuration diagram of a conventional cooking utensil.

13A and 13B are views showing an example of experimental data of the cooking appliance.

FIG. 14 is a diagram showing another example of experimental data of the cooking utensil.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Heating chamber 5 Environmental physical quantity detection means 11 Temperature estimation means 12 Control means 13 First heating supply means 14 Second heating supply means

 ─────────────────────────────────────────────────── ─── Continuation of the front page (72) Inventor Nakason Son 10-1 Higashisanda, Tama-ku, Kawasaki-shi, Kanagawa Matsushita Giken Co., Ltd.

Claims (4)

[Claims] 1. A heating chamber for storing food for cooking, first and second heating supply means for heating the food, environmental physical quantity detection means for detecting an environment in the heating chamber, and A temperature estimating means for estimating the surface temperature and the backside temperature of the cooked food based on the output of the environmental physical quantity detecting means, and a control means for controlling the first and second heating supply means based on the output of the temperature estimating means. And the control means is
Only the first heating and supplying means is heated until the back surface temperature output of the temperature estimating means reaches a predetermined value T1, and thereafter only the second heating and supplying means is heated, and the surface temperature of the temperature estimating means is increased. A cooking utensil in which heating of the first and second heating supply means is stopped when the output reaches a predetermined value T2. 2. The temperature estimation means internally includes a plurality of connection weight coefficients of a fixed neural network for estimating the temperature of a food item obtained by a method simulating a neural network composed of a plurality of neural elements. The cooking utensil according to claim 1, further comprising a neural network model means included in. 3. The cooking utensil according to claim 1, wherein the temperature estimating means comprises a hierarchical neural network schematic means constructed by combining layers composed of a plurality of neural elements in multiple layers. 4. The cooking utensil according to claim 1, wherein the environmental physical quantity detecting means includes a temperature detecting means for detecting an ambient temperature in the heating chamber.