JP2926444B2 - rice cooker - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a rice cooker used for the purpose of stably providing delicious rice in any situation by utilizing neuro control for controlling the amount of heating during cooking.

[0002]

2. Description of the Related Art Conventionally, the heating amount of a rice cooker has generally been determined by taking the output of a rice cooker sensor during heating by a rice cooker, making a determination using a microcomputer, and determining the heating amount. As this method, Japanese Patent Publication No. 59-53
No. 048, JP-A-59-57616, JP-A-59-155222, etc., use the principle that when the amount of cooked rice is small, the temperature rise of the rice cook sensor rises sharply as the amount of cooked rice is reduced. And the first technology, and Japanese Patent Publication No. 60-1007, invented to solve these problems.
Japanese Patent Application Laid-Open No. 60-18128 discloses a second technical idea of controlling the amount of heating while comparing the target temperature with the output of the rice cooking sensor. The present invention is a further development of the second technique.

[0003]

Cooking rice is an operation of heating rice, and the ideal way to raise the temperature over time is determined by theory. However, in the first technique, since the amount of cooked rice is determined based on a rise in temperature when heating with constant power, the temperature rises in a shorter time as the amount of cooked rice is smaller, which is different from the theory. There are different problems.

In order to solve this problem, the second technique controls a rice cooker by comparing a target temperature to be generated with an output of a rice cooker sensor. If the rice-cooking sensor accurately detects the temperature of rice, the smaller the amount of cooked rice, the smaller the input power, and the temperature rise characteristic as always theoretical can be obtained regardless of the amount of cooked rice. However, since the rice cooker sensor is in contact with the inner pot and the inner pot is heated by the rice cooker, it actually detects a temperature different from the temperature of rice. The temperature of the rice cooker rises when heated by the rice cooker, and falls when the heating is stopped. Therefore, simply controlling whether the temperature of the rice cooker is higher or lower than the target temperature will inevitably cause a deviation from the actual water temperature, making it difficult to perform theoretical heating regardless of the amount of cooked rice.

[0005]

SUMMARY OF THE INVENTION The present invention has been made to solve the above-mentioned problems, and has been made in consideration of the difference between the target temperature output from the target temperature generator and the output of the rice cooker sensor at predetermined time intervals. A shift condition judging unit for detecting
A change rate judging unit that detects a difference between the output of the rice cook sensor a predetermined time ago and the current rice cook sensor, and a neural network based on two inputs from the shift degree judging unit and the change rate judging unit to the rice cooker. A control unit for determining the amount of energization is provided.

[0006] In addition to the input from the shift condition judging unit and the change rate judging unit, the effect is large based on the input of the room temperature, lid temperature, voltage, menu, hardness preference selection signal and the like. .

Further, it is effective to provide a side heater and a lid heater in addition to the rice cooking heater, and determine the amount of power to these heaters based on the output of the neural network.

[0008]

According to the configuration of the present invention, not only the target temperature and the [degree of deviation] of the output of the rice cooker sensor, but also whether the temperature of the rice cooker is rising or falling due to the heating up to now is determined. The rate of change, whether the slope is slow or steep, is also used as a source of judgment in the neural network. For example, if the degree of deviation is lower than the target temperature and the rice cooker is lower than the target temperature, it can be determined that the amount of heating up to now is small. Increase. However, if the [rate of change] of the rice cook sensor rises sharply, even if the amount of heating is reduced, the rice cook sensor predicts that the temperature will be higher than the target temperature after the next fixed time, and the heating amount is reduced. . In addition, the neural network that has once learned to judge by "shift condition" and "change rate" is not learned in addition to "shift condition" and "change rate". Room temperature, lid temperature, voltage, menu, hardness Based on the input of the preference selection signal or the like, the amount of current to be supplied is determined so as to complement appropriately according to the learned pattern and to provide a more appropriate amount of heating.

Further, the neural network does not require a rule that can be expressed as an equation between various combination patterns, and if a situation that occurs when rice is cooked by energizing a rice cooker is learned, a side heater and a lid heater can be obtained. Even in the energization of the power supply, appropriate control that actually applies is performed.

[0010]

DESCRIPTION OF THE PREFERRED EMBODIMENTS One embodiment of the present invention will be described below in detail with reference to the drawings. 1 is a sectional view of a rice cooker according to an embodiment of the present invention, FIG. 2 is a configuration diagram of a heating control system of the rice cooker, and FIG.
7A and 7B are diagrams showing an example of a temporal change in heating control by the rice cooker, wherein FIG. 7A shows a case where the amount of cooked rice is large, and FIG. FIG. 4 is a diagram showing an example of a neural network.
FIG. 5 is a diagram showing neuron elements constituting a neural network. In FIG. 1, a rice cooker 2 is attached to a bottom portion inside a main body 1, and an inner pot 3 is detachably inserted into the main body 1, and the inner pot 3 rides on the rice cooker 2 to determine a position. The rice cooking sensor 4 attached to the main body 1 comes into contact with the inner pot 3 and comes into close contact with the force of the spring 5. A control unit 6 is provided inside the main body 1, and the rice cooker sensor 4 and the rice cooker 2 are connected to the control unit 6.

As shown in FIG. 2, the control section 6 includes a time measuring means 7 for measuring a predetermined time, a target temperature generating section 8 for generating a target temperature to be reached by the rice cooker 4 in accordance with the time measured by the time measuring means 7, and a target. A shift condition judging unit 9 for obtaining a difference (shift condition) between the target temperature output from the temperature generation unit 8 and the rice cooker sensor 4, and stores the output of the rice cooker sensor 4 at regular time intervals of the timekeeping means 7, A change rate judging section 10 for comparing the output values of the rice cooking sensor to obtain a change rate during a certain period of time, a neural network 11 having the shift degree judging section 9 and the change rate judging section 10 as inputs, and a heating amount which is an output of the neural network 11. A heating control unit 12 that receives power from the rice cooker 2 and a power supply control unit 13 that controls power supply to the rice cooker 2 based on a signal from the heating control unit 12 are provided.

Before describing the operation along the procedure, the neuro control will be described a little. Fig. 4 Neuro control
By inputting a numerical value from the input layer (left side) of the neural network 11 having the structure shown in FIG. 1, the numerical value is transmitted between the networks, and a result is output to the output layer (right side). The network is composed of a plurality of neuron elements and signal lines connecting these, and one neuron element has a configuration shown in FIG.

The operation of the neuron element will be described with reference to FIG. The neuron element has a plurality of input terminals and one output terminal, and the signal line connected to the output terminal is connected to the output terminal of each neuron element in the upper layer. Each signal line has a transfer coefficient (weight: W). This means that the signal is amplified and attenuated and transmitted, and the signal I is transmitted W times. The value obtained by adding the threshold value Ws to the sum of the values transmitted to the respective input terminals and processing with the sigmoid function is the output of the neuron element.

When the above processing is represented by an arithmetic expression,

[0015]

(Equation 1)

Now, the values of the inputs I1 to In are arbitrarily determined.
By changing the weighting: W and the threshold: Ws, the output Z
You can see that also changes. Learning is the process of manipulating W and Ws so that the value of Z should be as expected for the input. Actually, a network is formed as shown in FIG. 4, and various input patterns (I1 to I1) are obtained by changing the weight of signal lines of the entire network and the threshold value of each neuron.
(correlation of n) to learn to always output the correct answer at that time. Therefore, there are many input patterns to be learned,
The number of neuron elements constituting the network is required to be large so that the relation with the correct answer output is nonlinear and there is no rule. Once W and Ws are determined, a solution can be obtained from the equation even for an input pattern that has not been learned. As described above, the neural network expresses the learning result by manipulating the values of W and Ws. Since each of the values of W and Ws has no meaning, the neural network is hereinafter treated as a black box.

[0017] Generally, a neurocomputer has a function of operating the values of W and Ws by learning, but a computer used for controlling a rice cooker as in the present invention is equipped with a neural network of learning results. Since the values of W and Ws are invariable, it cannot be said that "the smarter the more they are used," however, if the network is a sufficiently learned result, it is possible to perform control utilizing the effect of neuro control.

The operation will be described below. When the user puts rice and water into the inner pot 3 and inserts it into the main body 1 and operates a rice cooker switch (not shown), rice cooker starts. At the start of rice cooking, first, the output of the rice cooking sensor 4 is input to the target temperature generator 8 and the change rate determiner 10. The target temperature generation unit 8 generates a target temperature to be reached after a certain time based on the input initial output of the rice cooking sensor 4. This target temperature may be read from table data storing an ideal rice cooking curve, or a constant value that rises along the rice cooking curve may be added.

On the other hand, the change rate judging section 10 stores the input rice cooker sensor output as an initial value. For example, if the output of the rice cooker sensor 4 is 20 ° C., 20 ° C. is stored in the change rate determination unit 10, and the target temperature is 24 ° C. obtained by adding 4 ° C. to 20 ° C.

Next, upon receipt of a signal from the heating control unit 12, the energization control means 13 operates, and the energization of the rice cooker 2 is started, and at the same time, the timing of the timekeeping means 7 is started. In the present embodiment, the power supply control means 13 is constituted by a relay, and the heating amount is controlled by changing the ratio of the ON time to the OFF time during a certain period. During the first fixed time, heating is performed with a predetermined fixed heating amount. For example, the time measured by the time measuring means 7 is 1 minute, and the first constant heating is ON for 30 seconds and O for 30 seconds.
FF. When the counting of one minute by the timing means 7 is completed, the output of the rice cooking sensor 4 is shifted to the deviation determining unit 9 and the change rate determining unit 1.
0, the difference between the target temperature and the output of the rice cooker sensor 4 is calculated, and the difference between the output of the rice cooker sensor 4 before and the current time is calculated.

At the same time, the temperature stored in the change rate judging section 10 is rewritten into the output of the rice cooking sensor 4. For example, 1
Assuming that the rice cooker sensor 4 has reached 23 ° C. due to heating for one minute, the “deviation” is −1 of the difference from the target temperature of 24 ° C.
° C, “change rate” is + 3 ° C, the difference from the stored 20 ° C
It is. After calculating the change rate, 23 ° C. is stored in the storage unit of the change rate determination unit 10 that previously stored 20 ° C. The [shift] and [rate of change] are calculated using the neural network 1
When input to 1, output is made according to the learning result. This output may be learned so as to directly output the next heating amount, or may be learned so as to output a correction value for correcting the electric power amount with respect to the result of the current heating.

If the present embodiment outputs a correction value, for example, +5 is output from the input of the network. Upon receiving this output, the heating control unit 12 outputs the heating amount 3
5 seconds are added to 0 seconds, and the heating control of ON for 35 seconds and OFF for 25 seconds is started. At the same time, the timer 7 is reset and the timer for 1 minute is started again. Further, the target temperature generation unit 8 generates 28 ° C. obtained by adding 4 ° C. to the previous target temperature of 24 ° C. as the target temperature one minute later.

One minute later, it is assumed that the rice cooking sensor 4 is at 30 ° C. By the same procedure, the deviation is + 2 ° C from 28 ° C
The rate of change is 23 ° C. + 7 ° C. In this case, since the temperature is higher than the target temperature and the rate of change indicates a rapid rise, it is predicted that the deviation from the target will be further increased in the next minute after the next power is not extremely reduced. Since such a situation is also learned as an input pattern, the output from the neural network 11 is, for example, -25. Heating amount 3
10 obtained by adding -25 to 5 seconds becomes the next heating amount, and 10 seconds O
N. Heating for 50 seconds OFF is performed.

Hereinafter, the amount of heating can be similarly controlled by increasing the target temperature at regular intervals. Although the actual water temperature / rice temperature and the output temperature of the rice cooker sensor 4 are different, there is a correlation depending on the amount of cooked rice. When the amount of cooked rice is small, the target temperature can be reached with a small amount of heating. When the amount of cooked rice is large, the amount of heat escaping from the inner pot 3 to water and rice is increased with a small amount of heating, so that the output of the cooked rice sensor 4 does not reach the target temperature or falls even if it has reached the target temperature. The result is that the amount of heating is applied.

Further, if the target temperature is fixed at a constant temperature for a while and then raised again, the amount of heating during the fixed period is the minimum amount of heating required to maintain that temperature, and the efficiency of rice production is reduced to lukewarm water. It is also possible to achieve a "preheated cooking" effect of absorbing water well.

As described above, in the present embodiment, the rice cooking sensor 4 is controlled so as to rise in accordance with the target temperature generated by the target temperature generating section 8. In addition to the simple binary comparison, the neural network 11 learns the degree of rise of the rice cooker sensor 4 due to overheating and the degree of fall of the rice cooker sensor 4 due to insufficient heating. An ideal way of increasing the temperature in accordance with the theory becomes possible. As a result, the same finished rice is always obtained. Since the output of the neural network 11 is also interpolated in addition to the learned input pattern, the amount of heating changes finely even with a slight difference in water level, so that the same finished rice is always obtained.

Further, the neural network 11 includes all or one of [room temperature], [lid temperature], [voltage], [menu], [hardness preference selection signal] selected by a switch or the like according to the customer's preference. If the optimum output is learned under these input conditions by also inputting the section, the heating amount control according to the input becomes possible. Further, a stable finish according to the customer's preference can be provided.

In this embodiment, only the heating amount of the rice cooker 2 is set as the output of the neural network 11. However, as another embodiment, as shown in FIG.
If the heating amount is controlled by the output of the network, the heating can be performed from the entire circumference of the inner pot without unevenness, and the heating balance can be controlled by learning the network.

[0029]

According to the present invention, the amount of electricity supplied to the rice cooker by the neural network is determined based on not only the difference between the target temperature and the output of the rice cooker sensor but also the change rate of the output of the rice cooker a certain time ago and the present. Because it was decided, regardless of the amount of rice cooked, the ideal temperature rise that matches the rice cooking theory was achieved, and the same finished rice was always cooked,
Furthermore, even with a slight difference in water content, the amount of heating changes minutely, and the effect is that the same finished cooked rice can always be obtained.

A similar effect can be obtained in a neural network in which the amount of power to the rice cooker is determined by inputting a room temperature, a lid temperature, a voltage, a menu, a hardness selection signal, and the like in addition to the deviation degree and the change rate. The same effect can be obtained by providing a side heater on the side surface of the main body and a lid heater on the lid side and controlling the amount of heating by a neural network.

[Brief description of the drawings]

FIG. 1 is a sectional view of a rice cooker showing one embodiment of the present invention.

FIG. 2 is a configuration diagram of a heating control system of the rice cooker.

FIG. 3 is a diagram showing an example of heating control of the rice cooker. (A) When the amount of cooked rice is large. (B) When the amount of cooked rice is small.

FIG. 4 is a diagram showing an example of a neural network.

FIG. 5 is a diagram showing neuron elements constituting a neural network.

FIG. 6 is a sectional view of a rice cooker according to another embodiment.

[Explanation of symbols]

 2 Rice Cooker 2a Side Heater 2b Lid Heater 4 Rice Cooker Sensor 6 Control Unit 7 Timekeeping Means 8 Target Temperature Generator 9 Deviation Condition Judgment Unit 10 Change Rate Judgment Unit 11 Neural Network

Claims (3)

(57) [Claims] 1. A main body, an inner pot stored in the main body, a rice cooking sensor provided in the main body and in close contact with the inner pot to detect a temperature of the inner pot, a rice cooker provided in the main body and heating the inner pot, In a rice cooker having a time measuring means for measuring a fixed time and a target temperature generator for generating a temperature characteristic of an ideal rice cooking sensor along with the progress of rice cooking, the predetermined time of the predetermined time measuring means (7) A deviation degree determination unit (9) that detects a difference between a target temperature output from the target temperature generation unit (8) and an output of the rice cooking sensor (4), and a current rice cooking sensor (4) a predetermined time before and A change rate judging section (10) for detecting an output difference of the rice cooker with a neural network (11) based on two inputs from a shift degree judging section (9) and a change rate judging section (10). A rice cooker provided with a control unit (6) for determining the amount of electricity supplied to (2) . 2. The neural network (11) has a room temperature, a lid temperature, a voltage, a menu, and a hardness in addition to an input from a shift degree judging section (9) and a change rate judging section (10). 2. The rice cooker according to claim 1, wherein a preference signal is input to determine the amount of electricity supplied to the rice cooker. 3. A side heater (2a) on the side of the main body and a lid heater (2b) on the main body lid separately from the rice cooker (2),
The rice cooker according to claim 1 or 2, wherein the amount of electricity to the side heater (2a) and the amount of electricity to the lid heater (2b) are determined by the output of the neural network (11).