JPH05154041A - Rice boiler - Google Patents

Abstract

PURPOSE:To boil throughly rice by providing a learning type electrification rate determining means to change boiling maintaining electrification rate on and after the next boiling according to the boiling result after the completion of the rice boiling. CONSTITUTION:An electrification rate determining means 10 is a fuzzy reasoning one for inputting a temperature ride rate and outputting the electrification rate. The temperature rise rate can be easily obtained by differentiating the output of a temperature detecting element. A neuro-fuzzy learning means 11 has a boiling time measured by a boiling time measuring means 9 as an input to alter the fuzzy resoning rule of the electrification rate determining means 10. Every time rice boiling is completed, a learning type electrification rate determining means 8 alters the relationship between input and output used on and after the next rice boiling according to a rice boiling amount on the basis of the rice boiling result. Thus, every rice boiler can learn respectively the optimum electrification rate to operate as the rice boiler capable of boiling throughly rice required by users.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a rice cooker that enables on-site learning of the energization ratio during rice cooking.

[0002]

2. Description of the Related Art Conventional so-called microcomputer rice cookers maintain boiling at an energization ratio determined by the manufacturer to be optimum. That is, the energization ratio determining means determines the energization ratio according to the amount of the food to be cooked. The energization ratio is determined according to the input / output relationship such as.

[0003]

However, since there are subtle deviations in the position of the temperature sensor and subtle differences in the heating characteristics of the heating means depending on the individual rice cookers, the conventional rice cookers are suitable for the individual rice cookers. There was a problem that fine rice could not be cooked. Further, there is a problem that the energization ratio cannot be changed depending on the preference of the user or the use environment.

Therefore, the present invention is intended to solve such a conventional problem, and after the completion of rice cooking, the rice cooking conditions for the next time and thereafter are changed according to the result of the rice cooking, and the rice cooker can cook rice finely. The primary purpose is to provide.

[0005] Also, to provide the second, third, fourth, fifth, sixth and seventh means for achieving the first object, it is the second, third, fourth, sixth and sixth.・ It is the seventh purpose.

[0006]

The first means of the present invention for achieving the above-mentioned first object is a heating means, a control means for controlling an energization ratio of the heating means, and a temperature of an object to be cooked. A rice cooker equipped with a temperature detecting element for detecting, a time measuring means for measuring time, and a learning type energization ratio determining means for determining the energization ratio according to the amount of the food to be cooked according to the rice cooking results up to the previous time. To do.

The second means of the present invention for attaining the second object is, in addition to the configuration of the first means of the present invention, a boiling time measurement for measuring the boiling maintaining time from the detected value of the temperature detecting means. The rice cooker is provided with means, and the measured value of the boiling time measuring means is input to the learning type energization ratio determining means.

A third means of the present invention for achieving the third object is, in addition to the configuration of the first means of the present invention,
The rice cooker is provided with an evaluation value input means for inputting the evaluation value of the rice cooking result, and the input value of the evaluation value input means is input to the learning type energization ratio determining means.

A fourth means of the present invention for achieving the fourth object is, in addition to the configuration of the first means of the present invention, a learning type energization ratio determining means and an energization ratio determining means. The rice cooker is equipped with learning means for changing the input / output characteristics of the ratio determining means in accordance with the previous rice cooking results.

A fifth means of the present invention for achieving the fifth object is, in addition to the configuration of the first means of the present invention,
As the learning type energization ratio determining means of the first means, an energization ratio initial value determining means for determining an initial value of the energization ratio, an energization ratio correction value determining means for determining a correction value for correcting the initial value, and The rice cooker is equipped with learning means for changing the input / output characteristics of the energization ratio correction value determining means in accordance with the rice cooking results up to the previous time.

A sixth means of the present invention for achieving the sixth object is, in addition to the configuration of the first means of the present invention, a fuzzy reasoner and a fuzzy inference as learning type energization ratio determining means. The rice cooker is equipped with a neuro-fuzzy learning means for changing the inference rules of the rice cooker.

The seventh means of the present invention for achieving the seventh object is, in addition to the configuration of the first means of the present invention,
The learning type energization ratio determining means is a rice cooker including a neural network and a neural network learning means for changing the neural network.

[0013]

The operation of the first means of the present invention is as follows. That is, the rice cooker initially has an energization ratio according to the amount of cooked rice as an input / output relationship. Each time the rice is cooked every time, the learning-type energization ratio determining means changes the input / output relationship used for the next rice cooking according to the rice cooking result. Therefore, each rice cooker can learn the optimal energization ratio for each rice cooker, and acts as a rice cooker capable of finely cooked rice according to the user's request.

In the second means of the present invention, the operation of the boiling time measuring means is added to the operation of the first means of the present invention. In other words, a certain amount of boiling maintenance time is required for the gelatinization of rice. Therefore, the boiling time measured is adopted as the input of the learning type energization ratio determining means, and therefore,
From the next time on rice cooking, the energization ratio can be changed to optimize the boiling time.

The third means of the present invention is the addition of the operation of the evaluation value input means to the operation of the first means of the present invention. That is, after cooking rice, the user inputs the evaluation value for the cooking result into the evaluation value input means. The input evaluation value is adopted as an input of the learning-type energization ratio determining means, and thus acts as a rice cooker that can cook rice according to the taste of the user.

Further, the fourth means of the present invention is provided with the energization ratio determining means and the learning means as the learning type energization ratio determining means, and the learning means optimally changes the input / output relationship of the energization ratio determining means. It works as a go-to rice cooker.

The operation of the fifth means of the present invention is as follows. That is, the learning-type energization ratio determining unit includes an energization ratio initial value determining unit, an energization ratio correction value determining unit, and a learning unit. Therefore, by adjusting the correction value by the learning means, the learning means functions as a rice cooker that optimally changes the input / output relationship of the energization ratio determining means with a small-capacity program.

The sixth means of the present invention operates as follows. That is, the learning type energization ratio determining means includes a fuzzy reasoner and a neuro-fuzzy learning means. By changing the fuzzy inference rule according to the teaching data, this neuro-fuzzy learning means can optimize not only the amount of rice cooked at that time, but also the energization ratio to the amount of rice cooked near the amount of rice cooked at that time. ..

The operation of the seventh means of the present invention is as follows. That is, the learning type energization ratio determining means includes a neural network and a neural network learning means. Neural network learning means,
The neural network is optimally changed according to the teaching data.

[0020]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The overall construction of a rice cooker which is a first embodiment of the present invention will be described below with reference to FIG. Reference numeral 1 denotes a main body, and a bottom portion is provided with a heating means 3 including a heater and a triac or a heater and a current control circuit, and a control means 7 for controlling the heating means 3. Also, the lid 5 is on the top and the inner pot 2 is on the inside.
have. A temperature detecting element 4 such as a thermistor for detecting the temperature of the food to be cooked is provided at the center bottom of the inner pot 2. Further, the main body 1 inputs the time measuring means 6 for measuring the time, the boiling time measuring means 9 for measuring the boiling maintaining time using the time measuring means 6 based on the information of the temperature detecting element 4, and the measured value of the boiling time measuring means 9. The first learning type energization ratio determining means 8 is provided. The clock means 6, the control means 7, the first learning type energization ratio determining means 8 and the boiling time measuring means 9 are constituted by a microcomputer in this embodiment.

Next, the construction of the first learning type energization ratio determining means 8 will be described with reference to FIG. The energization ratio determining means 10 is a fuzzy reasoner that inputs the temperature rise rate and outputs the energization ratio. This temperature increase rate can be easily obtained by differentiating the output of the temperature detection element 4.
The neuro-fuzzy learning means 11 changes the fuzzy inference rule of the energization ratio determining means 10 with the boiling time measured by the boiling time measuring means 9 as an input.

An embodiment of the fuzzy reasoning device which is the energization ratio determining means 10 will be described with reference to FIGS. The inference rule is, for example, "if the temperature rise rate is small, the energization ratio is increased" and is composed of three rules shown in FIG. The qualitative concept that the temperature rise rate is “small” or the energization ratio is “large” is quantitatively expressed by the membership function shown in FIG.

Next, an embodiment of the neuro-fuzzy learning means 11 will be shown. With the boiling time of 20 minutes as an ideal condition, the boiling time measured by the boiling time measuring means 9 is compared with this 20 minutes. If the boiling time is shorter than 20 minutes, it means that the energization ratio was too large, so the energization ratio is lowered from the next time. As the teaching data for learning, the temperature increase rate input to the energization ratio determination means 10 at this time and the data of the ideal energization ratio are set. Using this teaching data, fuzzy reasoning rules and membership functions are optimally set by the steepest descent method (one of learning rules used in neural networks, which is a method of minimizing an error function). From the next rice cooking, the energization ratio is determined using the newly set fuzzy inference rule.

As described above, according to the present embodiment, by inputting the boiling maintaining time, the energization ratio is determined according to the characteristics of each rice cooker, and the energization according to the usage conditions such as voltage and temperature is performed. The ratio can be determined. Also, the energization ratio determining means is a fuzzy reasoner, and the fuzzy inference rule is changed by the neuro-fuzzy learning means, so that each time rice is cooked, not only the amount of cooked rice at that time but also the amount of cooked rice near the amount of cooked rice at that time is energized. The ratio is optimized.

In the present embodiment, the consequent part variable of the fuzzy inference is a general triangular type, but even when it is realized by a real value, it can be optimized by the steepest descent method.

Next, the overall construction of the rice cooker according to the second embodiment of the present invention will be described with reference to FIG. Reference numeral 12 is an evaluation value input means for the user to input an evaluation value for the finished product etc. after the rice is cooked. 13 is an evaluation value input means 1
The second learning type energization ratio determining means receives the evaluation value input in 2. Second learning type energization ratio determining means 13
Is composed of a microcomputer in this embodiment. It should be noted that the members having the same functions as those in the first embodiment have the same numbers as those used in FIG. 1 and the description thereof will be omitted.

Next, the configuration of the second learning type energization ratio determining means 13 will be described with reference to FIG. In this embodiment, the energization ratio is calculated as (initial value + correction value). Only this correction value is changed by learning. Further, the energization ratio initial value determination means 14 outputs the initial value of the energization ratio with the temperature rise rate as an input. Energization ratio correction value determination means 1
Reference numeral 5 is a neural network which outputs the correction value of the energization ratio by inputting the temperature rise rate. The neural network learning unit 16 changes the neural network of the energization ratio correction value determination unit 15 by using the evaluation value input to the evaluation value input unit 12 as an input.

Next, the neural network learning means 1
6 shows an example. After cooking rice, the user inputs the evaluation value 1
Enter the evaluation value of the rice cooking result in 2. For example, when the evaluation that it was sticky was obtained, the energization ratio for maintaining boiling was low, so the ideal value of the energization ratio was slightly higher than the energization ratio used at this time. The teaching data for learning is a set of data of the temperature increase rate input to the energization ratio correction value determination means 15 at this time and the difference between the ideal energization ratio and the initial value. Using this teaching data, the neural network is optimally set by the steepest descent method. From the next rice cooking, the energization ratio is determined using the newly set neural network.

As described above, according to the present embodiment, the evaluation value input means is provided to input the evaluation value for the rice cooking result.
The energization ratio can be determined according to the preference of the user. Further, since the energization ratio is set to (initial value + correction value) and only the correction value is learned, learning can be realized by a small-capacity microcomputer program. The energization ratio correction value determining means is a neural network, and the neural network learning means is configured to change the neural network, so that each time rice is cooked, not only the amount of rice cooked at that time but also the amount of rice cooked near the amount of rice cooked at that time is energized. The ratio is optimized.

[0030]

As described above, according to the first means of the present invention, by mainly comprising the learning type energization ratio determining means, the boiling can be maintained after the next cooking depending on the result of the cooking. It is possible to realize a rice cooker that can change the energization ratio and cook rice with fine texture.

According to the second means of the present invention, a boiling time measuring means for measuring the boiling maintaining time mainly based on the detected value of the temperature detecting means is provided, and the measured value of the boiling time measuring means is optimized. By doing so, it is possible to determine the optimum energization ratio and to realize a rice cooker capable of finely cooked rice.

According to the third means of the present invention, mainly,
By providing the evaluation value input means for inputting the evaluation value of the rice cooking result and using the input value of the evaluation value input means as the input of the learning type energization ratio determination means, the energization ratio that suits the user's preference is determined. Therefore, it is possible to realize a rice cooker capable of finely cooking rice.

Further, according to the fourth means of the present invention, the energization ratio is finely tuned by mainly comprising the energization ratio determining means and the learning means for learning the input / output relationship of the energization ratio determining means. It is possible to realize a rice cooker that can learn and cook finely cooked rice.

Further, according to the fifth means of the present invention, mainly an energization ratio initial value deciding means for deciding an initial value of the energization ratio and an energization ratio correction value deciding means for deciding a correction value for correcting the initial value. With a learning-type energization ratio determining means configured by and learning means for changing the input / output characteristics of the energization ratio correction value determining means according to the rice cooking results up to the previous time, a small capacity program can be realized. The learning means can optimally change the input / output relation of the energization ratio determining means, and can realize a rice cooker capable of finely cooking rice.

According to the sixth means of the present invention, a learning type energization ratio determining means mainly comprising a fuzzy reasoner and a neuro-fuzzy learning means for changing the inference rule of the fuzzy reasoner is provided. By,
Every time rice is cooked, not only the amount of cooked rice at that time but also the energization ratio to the amount of cooked rice near the amount of cooked rice at that time can be optimized. In addition, by using fuzzy inference, it is possible to realize a rice cooker that can understand the learning process easily, can be easily calculated, and can cook fine-tuned rice.

Further, according to the seventh means of the present invention, the learning type energization ratio determining means is mainly composed of a neural network and a neural network learning means for changing the neural network, whereby a complicated input / output relation is obtained. It is possible to realize a rice cooker that can learn rice and cook finely cooked rice.

[Brief description of drawings]

FIG. 1 is a schematic cross-sectional view showing the overall configuration of a rice cooker that is a first embodiment of the present invention.

FIG. 2 is a block diagram showing the first learning type energization ratio determining means.

FIG. 3 is a diagram showing an inference rule of a fuzzy inferencer which is the same energization ratio determining means.

FIG. 4 is a diagram showing membership functions of the antecedent part and the consequent part of the inference rule of the fuzzy reasoner.

FIG. 5 is a schematic cross-sectional view showing the overall configuration of a rice cooker that is a second embodiment of the present invention.

FIG. 6 is a block diagram showing a second learning type energization ratio determining means.

[Description of Reference Signs] 3 heating means 4 temperature detecting element 6 timing means 7 control means 8 first learning type energization ratio determining means 9 boiling time measuring means 10 energization ratio determining means 11 neuro-fuzzy learning means 12 evaluation value input means 13 Second learning type energization ratio determining means 14 Energization ratio initial value determining means 15 Energization ratio correction value determining means 17 Neural network learning means

 ─────────────────────────────────────────────────── ─── Continuation of the front page (72) Haruo Terai, 1006 Kadoma, Kadoma City, Osaka Prefecture Matsushita Electric Industrial Co., Ltd. (72) Masafumi Sadaira, 1006 Kadoma, Kadoma City, Osaka Matsushita Electric Industrial Co.

Claims (7)

[Claims] 1. A heating means, a control means for controlling an energization ratio of the heating means, a temperature detecting element for detecting the temperature of the object to be cooked, a time measuring means for measuring time, and a quantity depending on the amount of the object to be cooked. A rice cooker equipped with a learning type energization ratio determining means for determining the energization ratio according to the rice cooking results up to the previous time. 2. The cooked rice according to claim 1, further comprising a boiling time measuring means for measuring a boiling maintaining time from a detected value of the temperature detecting means, and the measured value of the boiling time measuring means is inputted to the learning type energization ratio determining means. vessel. 3. The rice cooker according to claim 1, further comprising an evaluation value input means for inputting an evaluation value of the rice cooking result, wherein the input value of the evaluation value input means is input to the learning type energization ratio determining means. 4. The cooking rice according to claim 1, further comprising, as the learning-type energization ratio determining means, an energization ratio determining means and a learning means for changing the input / output characteristics of the energization ratio determining means in accordance with the rice cooking results up to the previous time. vessel. 5. A learning type energization ratio determining means, an energization ratio initial value deciding means for deciding an initial value of the energization ratio, an energization ratio correction value deciding means for deciding a correction value for correcting the initial value, and this energization. The rice cooker according to claim 1, further comprising learning means for changing the input / output characteristics of the ratio correction value determining means according to the rice cooking results up to the previous time. 6. The rice cooker according to claim 1, further comprising a fuzzy inference device and a neuro-fuzzy learning device for changing an inference rule of the fuzzy inference device as the learning type energization ratio determining device. 7. The rice cooker according to claim 1, further comprising a neural network and a neural network learning means for changing the neural network as the learning type energization ratio determining means.