JP2943467B2 - kitchenware - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

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

[0002]

2. Description of the Related Art Conventionally, this type of cooking utensil (here, a microwave oven) has been configured as shown in FIG. Hereinafter, the configuration will be described.

As shown in FIG. 1, a cooking utensil 1 is composed of a cooking chamber 2 for storing food, a heating means 3 for cooking the cooking food, a thermistor for detecting the ambient temperature of the cooking chamber 2, and the like. A temperature detecting means 4 and a control means 5 for controlling the heating supply means 3 based on information from the temperature detecting means 4. In order to perform automatic cooking with such a configuration, it is necessary to know the weight of the food, the initial temperature, and the like. For this purpose, the time from when the power is turned on until the output of the temperature detecting means 4 reaches a certain temperature was measured, the weight of the food was estimated based on this time length, and this time was multiplied by a constant k specific to each food. Time was the optimal cooking time.

[0004]

In such a conventional cooking device, the temperature of the atmosphere in the cooking chamber is detected, the weight of the food is determined based on the detected temperature, and the cooking time is determined. There was considerable variation. Further, since the optimum cooking time is obtained by multiplying by a constant k specific to each food, a food selection means corresponding to each food is required, and the selection operation is complicated and inconvenient. In addition, although a standard finished state can be realized, there is a user's favorite finished state of cooking, which cannot be realized.

[0005] The present invention solves the above-mentioned problems, and it is possible to eliminate the variation in the finished food by estimating the degree of cooking of the cooked food in real time using the environmental physical quantity in the heating chamber which can be actually measured and detected. It is an object of the present invention to provide a cooking utensil that simplifies an operation of selecting an object, improves usability, and realizes a finished state of a food according to a user's preference.

[0006]

SUMMARY OF THE INVENTION In order to achieve the above object, the present invention provides a cooking chamber for storing food to be cooked, cooking means for cooking the food, and an environment in the cooking chamber. Environmental physical quantity detecting means for detecting, finished strength input means for inputting a finished cooking strength, cooking degree estimating means for estimating the cooking degree of the food based on output information of the environmental physical quantity detecting means, and the cooking degree estimating Output means and control means for controlling the cooking means based on information of the finished strength input means, wherein the cooking degree estimating means is based on a method of modeling a neural network composed of a plurality of neural elements. And a neural network model having a plurality of connection weighting factors for estimating the degree of cooking of the obtained and learned food.

[0007]

According to the present invention, by inputting the environment information in the heating chamber from the environmental physical quantity detecting means to the cooking degree estimating means, the present invention learns the actual cooking environment to be actually cooked and a plurality of internal cooking environments are internally provided. The cooking degree estimating means having the neural network model means having the weighting coefficient estimates the cooking degree of the food every moment. The control means controls the cooking means based on the output of the cooking degree estimating means and the information of the finished strength input means, and ends the cooking.

[0008]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS One embodiment of the present invention will be described below with reference to FIGS. The same components as those in the conventional example are denoted by the same reference numerals, and description thereof is omitted.

In this embodiment, an example in which the present invention is applied to a microwave oven as a cooking appliance will be described. As shown in FIG. 1, the environmental physical quantity detection means 6 detects the environment in the cooking room. In the present embodiment, the temperature of the atmosphere in the cooking chamber is detected, and is constituted by a thermistor or the like. The voltage level detecting means 7 detects the voltage level of the commercial power supply voltage. The timer 8 counts the time from when the power is turned on. The operation means 9 includes a category selection key 10 for selecting a category of cooking and a cooking key 1 for starting / stopping cooking.
1 and finished strength input means 12 for determining the finished strength.

The cooking degree estimating means 13 is for estimating the cooking degree of the food based on the output of the environmental physical quantity detecting means 6, the voltage level detecting means 7, the time keeping means 8, and the category selection key 10, and the control means 5 is for cooking. The cooking means 3 is controlled based on the output of the degree estimating means 12 and the information from the finished strength input means. The cooking means 3 includes a heater and a blower fan, and is disposed in the cooking chamber 2. Reference numeral 14 denotes a display means for displaying cooking information and the like, and is constituted by a liquid crystal display unit. A / D converters 15 and 16 convert the outputs of the environmental physical quantity detector 6 and the voltage level detector 7 into digital signals. FIG. 2 shows the configuration of the display unit and the operation unit.

Means constituting the cooking degree estimating means 13 include:
Since the solution method used in the conventional control method cannot be applied, the method is configured by modeling an optimal neural network as a multidimensional information processing method. The present embodiment is a cooking degree estimating means 13 having a neural network model means having a plurality of connection weight coefficients therein for estimating the cooking degree of a food learned and acquired by a method modeling a neural network.
Is provided.

Factors that affect the quality of the cooked food include the initial temperature in the heating chamber, the initial temperature of the cooked food, the type of cooking (category), and the voltage level of the commercial power supply voltage. The quality varies greatly depending on those factors.

In a neural network for estimating the degree of cooking of a cooking product, a plurality of connection weighting factors are determined when the cooking is performed in an environment in which cooking is actually performed (an environment in which various factors described above are combined). Originally, we collected data on how long the cooking time to achieve the optimal state of completion and the ambient temperature in the heating room changed,
The correlation between the environmental data, the ambient temperature data in the heating chamber, and the cooking degree (optimum cooking time) data of the food can be obtained by learning the neural network model means.

[0014] The schematic means of the neural network to be used include:
Reference 1 (DE Ramelhart et al., 2 authors, edited by Shunichi Amari "PDP Model" Sangyo Tosho, 1989), Reference 2 (Kaoru Nakano et al., 7 "Basics of Neurocomputers")
Published by Corona Co., Ltd., P102, 1990), Shoko 6
There is a technique disclosed in Japanese Patent Publication No. 3-55106. Hereinafter, the configuration and operation of specific neural network model means will be described using a multilayer perceptron using an error back propagation method as an example of the most well-known learning algorithm described in Document 1.

FIG. 3 is a conceptual diagram of a neural element which is a structural unit of the neural network model. In FIG.
N is a pseudo-synaptic connection converter that simulates a synaptic connection of a nerve, and 2a is a pseudo-synaptic connection converter 21-2N
2b is a set nonlinear function, for example, a sigmoid function with a threshold value h, f (y, h) = 1 / (1 + exp (−y + h)) (Equation 1) ) Is a nonlinear converter for nonlinearly converting the output of the adder 2a. Although not shown for simplicity of the drawing, an input line for receiving a correction signal from the correction means is connected to the pseudo-synaptic coupling converters 21 to 2N and the nonlinear converter 2b. Further, the pseudo synapse connection converters 21 to 2N serve as connection weight coefficients of the neural network model. 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 in the signal processing mode will be described. The neural element has N inputs X1 to X
n and outputs one output. i-th input signal Xi
Is the i-th pseudo-synaptic coupling converter 2 indicated by a square.
In i, it is converted to Wi · Xi. N signals W1.X1 to N1 converted by the pseudo-synaptic coupling converters 21 to 2N
Wn · Xn enters the adder 2a, and the addition result y is sent to the non-linear converter 2b, and becomes the final output f (y, h).

Next, the operation in the learning mode will be described. In the learning mode, the pseudo-synaptic connection converters 21 to 2
N and conversion parameters W1 to Wn and h of the non-linear converter 2b.
Are corrected by the correction amounts ΔW1 to ΔW1 of the conversion parameters from the correction means.
Receiving the correction signal representing Wn and Δh, the correction is made as follows: Wi · ΔWi; i = 1, 2,..., Nh + Δh (Equation 2)

FIG. 4 is a conceptual diagram of a signal conversion means in which four of the above neural elements are connected in parallel. Needless to say, the following description does not specify the number of neural elements constituting the signal conversion means as four. In FIG. 4, reference numerals 211 to 244 denote pseudo-synaptic coupling converters,
Reference numerals 201 to 204 denote add-nonlinear converters that combine the adder 2a and the non-linear converter 2b described in FIG. In FIG. 4, as in FIG. 3, the illustration is omitted because it becomes complicated, but the input lines receiving the correction signal from the correction means are connected to the pseudo-synaptic coupling converters 211 to 244 and the addition nonlinear converter 20.
1 to 204 are connected. The pseudo synapse connection converters 211 to 244 also become connection weight coefficients. As for 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 backpropagation method is employed as the learning algorithm. Reference numeral 31 denotes the above-described signal conversion means. However,
Here, M neural elements receiving N inputs are arranged in parallel. Reference numeral 32 denotes a correction unit that calculates a correction amount of the signal conversion unit 31 in the learning mode.

The operation of learning the signal processing means will be described below with reference to FIG. Signal conversion means 31
Receives N inputs S _in (X) and M outputs S
_out Outputs (X). The correction means 32 outputs the input signal S _in
(X) and the output signal S _out (X), and receives M error signals δ from the error calculating means or the signal converting means at the subsequent stage.
_j Wait until (X) is input. Error signal δ
_i (X) is input and the correction amount is represented by ΔW _ij = δ _i (X) · S _iout (X) · (1−S _iout (X)) · S _jin (X) (i = 1 to N, j = 1 To M) (Equation 3), and sends the correction signal to the signal conversion means 31. The signal conversion means 31 corrects the conversion parameters of the internal nerve elements according to the learning mode described above.

FIG. 6 is a block diagram showing the configuration of a multilayer perceptron using a neural network model.
X, 31Y, and 31Z are signal conversion means composed of K, L, and M neural elements, respectively.
2Z is a correction means, and 33 is an error calculation means.

The operation of the multilayer perceptron configured as described above will be described with reference to FIG.
In the signal processing unit 34X, the signal conversion unit 31X
Input S _iin (X) (i = 1 to N) is received and output S
_jout (X) (j = 1 to k) is output. Correction means 32X
_Receives the signal S _iin (X) and the signal S _jout (X), and waits until an error signal δ _j (X) (j = 1 to k) is input. Following a similar process, the signal processing unit 34Y, made at 34Z, the final output from the signal converting means 31Z S _{ho ut}
(Z) (j = 1 to M) is output. Final output S
_hout (Z) is also sent to the error calculating means 33. In the error calculating means 33, the square error evaluation function COST
Based on (Equation 4), the ideal output T (T1,.
, TM) is calculated, and an error signal δ _h (Z) is sent to the correction means 32Z.

[0023]

(Equation 1)

Here, 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 calculates the correction signal. ΔW (Z) is sent to the signal conversion means 31Z, and the error signal δ (Y) is
Send to Y The signal conversion means 31Z outputs the correction signal ΔW (Z)
Modify internal parameters based on. Note that the error signal δ (Y) is given by (Equation 6).

[0025]

(Equation 2)

Here, W _ij (Z) is the signal converting means 31Z.
Is a conversion parameter of the pseudo-synaptic coupling converter. Hereinafter, similar processing is performed in the signal processing units 34X and 34Y. By repeatedly performing the above procedure called learning, the multilayer perceptron, when given an input, outputs an output that approximates the ideal output T well.

In the above description, a three-stage multilayer perceptron is used, but any number of stages may be used. Also, the method of correcting the conversion parameter h of the non-linear conversion means in the signal conversion means and the method of speeding up the learning known as the inertia term in the document conversion means are omitted for simplification of description, but this omission is omitted. The present invention described below is not restricted.

As described above, the neural network model means performs cooking environment data (initial temperature in the heating chamber, commercial power supply voltage level, type of cooking, initial temperature of the food, etc.), atmosphere temperature data in the heating chamber, and cooking. By learning the relationship with the degree of cooking (optimum cooking time) data of an object, it is possible to express in a natural manner a control method that is not easy to describe with simple rules. In this embodiment, the cooking degree estimating means 13 is configured by incorporating the information thus obtained.

More specifically, the cooking degree estimating means 13 is constituted by using only the signal converting means 31X, 31Y and 31Z of the multilayer perceptron after the learning is sufficiently completed as the neural network model means. The data actually learned will be described.

FIG. 7 shows the characteristics when the initial temperature of the cooking chamber 2 is low, the commercial power supply voltage is 100 V, and the type of cooking is an oven with a sponge cake diameter of 18 cm. FIG.
7A shows a change in the environmental physical quantity detection means 6 (atmospheric temperature in the heating chamber), and FIG. 7B shows a degree of cooking of the food. The cooking degree is indicated by a value in the range of 0 to 1, and is linearly increased near the completion of cooking so that the standard is 0.5, the weakness is 0.2, and the strongness is 0.8. This was determined by experiment. FIG. 8 shows the same characteristics as in FIG. 7 when the sponge cake diameter is 21 cm.

FIGS. 8 (a) and 8 (b) correspond to FIGS.
(B) respectively. It can be seen that the output voltage change (atmospheric temperature in the heating chamber) of the environmental physical quantity detection means 6 varies depending on the amount of the food. Similarly, when the power supply voltage is changed or the type of food is changed, the output changes again. The degree of cooking is determined experimentally under these conditions.

Such an experiment was carried out in the same manner for all combinations of environments when cooking actually. Then, the experimental data was input to the neural network model means for learning. That is, the neural network schematic means receives the ambient temperature information in the cooking chamber of the environmental physical quantity detecting means 6, the atmospheric temperature information one minute before the present time as the atmospheric temperature gradient information, and the commercial power supply voltage level information of the voltage level detecting means 7. And information on the elapsed time from power-on obtained by the timer 8, the category information obtained from the category selection key 10, and the cooking degree information of the food as an ideal output are input and learned. Inside signal conversion means 31X, 31Y, 31
Z are established, and they are incorporated in the cooking degree estimating means 13 as a neural network schematic means.

Next, the operation will be described with reference to the block diagram shown in FIG. First, put the food in the cooking room,
The cooking category is selected by the category selection key 10 from the operation means 9, and the finished strength is input from the finished strength input means 12. And cooking start key 1
1 is input. These pieces of information are input to the control unit 5 and the cooking degree estimation unit 13. The control means 5 outputs a signal to start time measurement to the time measurement means 8 and outputs a heating start signal to cause the heating supply means 3 to generate heat. The timing information of the timing means 8 is input to the cooking degree estimating means 13. The output of the environmental physical quantity detecting means 6 is digitally converted by the A / D converting means 15 and the environmental physical information (atmospheric temperature information) in the heating chamber is input to the cooking degree estimating means 13 every moment.

The voltage level information of the commercial power supply voltage from the voltage level detecting means 7 is digitally converted by the AD converting means 16 and inputted to the cooking degree estimating means 13. The cooking degree estimating means 13 estimates the cooking degree from time to time based on these input signals and information, and outputs the information to the control means 5. The control means 5 operates to control the heating supply means 3 based on the cooking degree information and the finished strength information obtained from the completed strength input means 12. That is, if the information obtained from the completed intensity input means 12 is a weak setting, the heating supply means 3 is stopped when the output of the cooking degree estimating means 13 exceeds 0.2. The heating / supplying means 3 is stopped, and if it is set to a higher value, the heating / supplying means 3 is stopped when the value exceeds 0.8.

Further, the control means 5, the time keeping means 8, and the cooking degree estimating means 13 are all constituted by 4-bit microcomputers, but they can be constituted by one microcomputer. The cooking degree estimating means 13 includes the temperature gradient information (two pieces of information of the current time and one minute before) of the environmental physical quantity detecting means 6 and the voltage level detecting means 7.
The information on the voltage level of the commercial power supply voltage obtained, the information on the time elapsed since the power was turned on obtained from the timer means 8, and the category information on the food obtained from the category selection key 10 are input. It is not binding on the invention.

Although ambient temperature information is used as environmental physical quantity information, it goes without saying that smoke information, burnt color information, humidity information, and steam information can also be applied. In this embodiment, a microwave oven is used as a cooking utensil, but a microwave oven or a gas oven may be used.

As described above, according to this embodiment, the cooking degree estimation incorporating the neural network model means having a plurality of connection weight coefficients of the neural network, learned in the environment of the cooking room where cooking is actually performed. Since the control means 5 controls the heating / supplying means 3 according to the finished intensity information set by the user and the estimated cooking degree by the completed intensity input means 12, there is no variation in the finished state. A simple cooking menu selection operation is not required, and usability can be improved. Further, the finished state of the cooked food can be adjusted to the user's preference.

[0038]

As is apparent from the above embodiments, according to the present invention, there is provided a cooking chamber for storing foods for cooking,
Cooking means for cooking the food; finished strength input means for inputting the finished strength of the food; environmental physical quantity detecting means for detecting an environment in the cooking chamber; and cooking based on the output of the environmental physical quantity detecting means. A cooking degree estimating means for estimating a degree of cooking of an object; and a control means for controlling the cooking means based on an output of the cooking degree estimating means and output information from the finished intensity input means. Has neural network schematic means having a plurality of connection weighting factors for estimating the degree of cooking of the food acquired and learned by a method of modeling a neural network composed of a plurality of neural elements, The cooking degree of the food can be estimated regardless of the initial temperature in the cooking chamber, the initial temperature of the food, the amount of the food, etc. The ability.

Further, the change of the environmental physical quantity has a characteristic peculiar to the cooking, and the cooking degree estimating means learned including the characteristic has no input for selecting the cooking corresponding to each cooking. The degree of cooking can be estimated by the category input integrated as a menu group, and there is an effect of improving operability.

Further, automatic cooking of a finished product desired by the user can be performed.

[Brief description of the drawings]

FIG. 1 is a block diagram showing the configuration of a cooking appliance according to one embodiment of the present invention.

FIG. 2 is a configuration diagram of an operation unit and a display unit used in the cooking appliance.

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

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

FIG. 5 is a block diagram of a signal processing unit employing an error back propagation method as a learning algorithm used in the cooking appliance.

FIG. 6 is a block diagram showing a configuration of a multilayer perceptron using a neural network model used in the cooking appliance.

FIG. 7A is a characteristic diagram showing an example of experimental data of the cooking appliance. FIG. 7B is a characteristic diagram.

FIG. 8A is a characteristic diagram showing another example of the experimental data of the cooking utensil;

FIG. 9 is a configuration block diagram of a conventional cooking appliance.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Cookware 2 Cooking room 3 Cooking means 5 Control means 6 Environmental physical quantity detection means 12 Finished strength input means 13 Cooking degree estimation means

──────────────────────────────────────────────────続 き Continued on the front page (72) Inventor Yoshihiro Ishizaki 1006 Kazuma Kadoma, Osaka Prefecture Inside Matsushita Electric Industrial Co., Ltd. No. 1 Matsushita Giken Co., Ltd. (56) References JP-A-1-139019 (JP, A) (58) Field investigated (Int. Cl. ⁶ , DB name) F24C 7/02 340

Claims (1)

(57) [Claims] 1. A cooking chamber for storing foods to be cooked, cooking means for cooking the foods, environmental physical quantity detecting means for detecting an environment in the cooking chamber, and a finished input of a finished cooking strength Intensity input means, cooking degree estimating means for estimating the cooking degree of the food based on the output information of the environmental physical quantity detecting means, output of the cooking degree estimating means, and cooking based on the information of the finished strength input means. Control means for controlling the means ,
The degree estimating means is a neural circuit composed of a plurality of neural elements.
Cooking acquired and learned by a method that models the road network
Multiple connection weighting factors for estimating the degree of cooking
A cooking utensil having a configuration with a neural network model .