A single square voltage pulse method(SSVP)has been used as an effective tool to study a great variety of human biophysical information and mind effects. The basic principles of this skin impedance measurement method can be understood in term of an equivalent electrical circuit model and power function for responsive current waveform. Then an two biophysically important parameters, AP and BP in this method. AP is related to the autonomic nervous system and BP is correlated with the blooflow. Many applications of a single square voltage pulse method is presented, some examples of which are responses to various kind of stimulus, audiovisual effects, color effects, biological rhythms, autonomic nervus functions, Zen and Qi-gong.

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Accurate assessment of dental developmental age is a prerequisite to obtain occlusal harmony and correct masticatory functiont. The feasibility of estimating the dental developmental age by a dental expert computer model based on a fuzzy system was previously reported. In this study a system based on a neural network model was developed. Both techniques were further adapted for practical application by inputting data readily obtainable by stmple clinical visual inspection. Six hundred and thirty five patients nanging in age from 27 to 184 months were used to optimize the systems. The dental ages of 50 additional patients estimated clinically by 2 expert pediatric dentists were compared to those obtained by the 2 optimized computer models. An average error of 11 to 18 months was observed with either computer model, while clinically the error averaged 12 to 14 months. The implementation of these methods allows assessment of dental age without exposing patients to X-rays with reasonable accuracy.

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This paper presents a rule-learning method for fuzzy production systems performing two-group classification where each fuzzy production rule consists of an antecedent clause and a consequent clause. For the antecedent clause a histogram of the results of an experiment is usually available. A possibility distribution assessing subjective belief can be acquired from experts, but such a possibility distribution can not be considered independently of the frequency of occurrence of events. On the other hand, the construction of the membership function in the consequent clause is often more difficult, because these membership functions must be constructed from the same universal set To overcome this problem we have used a rule-learning algorithm, Examples from two groups, linguistic conditional statements of experts and fuzzy properties in the antecedent clause, are required for this rule-learning algorithm. The fuzzy properties used in this algorithm are the possibility-probability consistency proposed by Zadeh and the specificity of a fuzzy set introduced by Yager. The proposed algorithm can construct the fuzzy production rules that fit a set of examples by using fuzzy properties relative to criteria of minimum entropy and a maximum distance of sample means. Our method provides a convenient rule-learning scheme in cases where the processes of membership function tuning and formalizing decision rules are very difficult or time consuming.

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We propose a mechanism explaining the high sensitivity and selectivity in sensorial natural neural networks. Related to this, we introduce a new paradigm in measurement, namely the concept of measurement based on chaos and we briefly present several sensor concepts, recently patented. The sensors have high sensitivity, and selectivity, while mimicking the natural sensing mechanisms. High sensitivity, measurement is achieved by using a nonlinear dynamic(chaotic)system and by monitoring the change of the global behavior of the system when the measured parameter changes.

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The subject of this paper will be the relationship between particularity in the beginnings of language learning in infancy and Zipf's law. Learning a language can be regarded as a process where an infant recognizes his/her own environment by words. The infant is in a transition from coarse-graining recognition of a known world to fine-graining one of an unknown world. This transution is irrational in terms of no necessity to step into unknown world from known world. Zipf's law is known as one of quantification expressing fractal structure and now-normal distribution, and a universal property of a phenomenon. In this study, however, the transition can be expressed a fixed point and quantified in term of Zipf's law obtained from a fixed point. On the other hand, in the plateau stage of learning a language, Zipf's law have not been observed, although it is said as a universal property of a phenomenon. The transition following the Ztpf's law suggests an appropriateness of the activity of the infant.

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Fuzzy multivariate analyses including linear regression analysis, fuzzy time-series analysis, fuzzy possibilistic linear model, etc. are formulated in terms of the extension principle. One objective of a fuzzy linear regression model is to build a model using fuzzy numbers which represent the possibilities included in the system. Therefore, the fuzzy regression model is also named a possibilistic regression model. Hitherto, it is hard for us to measure the goodness of a fuzzy regession model. As there is no index to evaluate the fuzzy regression model, it is not easy to evaluate the possibility of a fuzzy regression model. In this paper, we propose two indices to evaluate a fuzzy regression model. Our indices make us understand ike distribution of data and explain the relation of the distribution of data with the possibilistic interval of a fuzzy regression model The paper also exemplifies the characteristics of an Evaluation Index and builds an oral age model on ike basis of real data about the age and the number of sound teeth.

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The purpose of this study was to predict lymph node metastasis in tongue cancer. The subjects were 137 patients with squamous cell carcmoma of the tongue who had been treated at our department during the 20-year period from 1971 through 1990. We first used multivariate analysis and then fuzzy inference to assess the accuracy of these methods for predicting lymph node metastasis. Based on the results of the chi-square test and a correlation matrix, T category, N category, keratinization, mitosis and mode of invasion were selected as parameters for multivariate analysis. For the prediction using fuzzy inference, the patients were first divided into two groups: those treated surgically and those given radiotherapy. Three items, i. e. , tumor size, keratinization and mode of invasion were used for analysis. Then, an "If-Then rule" was applied to each of the two groups. It was found tha fhzzy inference has a higher accuracy for predicting lymph node metastasis than multivariate analysis. Fuzzy inference appears to be useful for predicting lymph node metastasis.

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We have already proposed a fuzzy reasoning method based on the distance between fuzzy sets. However, the distance type fuzzy reasoning method does not have a learning function. This paper discusses the benefits of providing the distance-type fuzzy reasoning method with a learning function. Unlike neural networks, GA, and other conventional approaches, this learning algorithm uses the features of the distance-type fuzzy reasoning method appropriately. Consequently, this algorithm is veg simple and extremely fast, requiring almost no learning time. Finally, the effectiveness of this learning algorithm is verified by simulation studies.

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Fuzzy inference has been applied for various types of clinical diagnosis, such as computer-aided diagnosis. Most of clinicians, however, tend to hesitate to adopt it, because some difficulties are inevitable to apply fuzzy inference actually. Although there are certain ways of thinking in clinical diagnosis, clinicians cannot transform them to fuzzy production rule in most cases. For general clinicians, it is very complicated to generate and prepare the membership functions. Consequently, computer-aided diagnosis using fuzzy inferrnce is diffcult to apply in the clinical field. Therefore, the authors would like to propose an adaptive neuro-fuzzy inference system(ANFIS)for the construction of a computer-aided diagnosis system. using ANFIS, the membership functions can be adjusted adequately in a neural-network principle from retrospective clinical data. Therefore, clinicians do not have to generate and verify the membership functions critically. In this paper, we report the attempt of ANFIS to ultrasonographic diagnosis and indicate its validity for diagnostic aid.

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Subjectivity, is inherent characteristics of information processing by/in humans, but almost no researcher has tackled this issue as main theme. Thus, this paper aims to give the readers a framework of subjective information processing(SIP)at first. Moreover, treatment of subjectivity in several natural sclences and relationships of SIP with related fields of engineering are described. Another aim is to show potential of SIP through an example, a model for commumicating subjective degree with adverbs of degree.

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This paper proposes an image segmentation method based on fuzzy if-then rules. It is a derivative of the conventional region growing method. This method represents expert's knowledge using fuzzy if-then rules, and embeds them as the growing criteria. To examine the proposed method, it has been applied to artificially generated images involving white Gaussian noise. In comparison with the conventional region growing method, the proposed method can segment region of interests(ROIs)with high robustness against to white noise. Moreover, it has been applied to dynamic mognetic resonance(MR)images of the Liver. The growing Criteria that represent physician's knowledge of MR images were derivedfrom the illustrated time-density curve of the liver, hepatic arteries, and veins after intravenous bolus injection. The experiments were done on three different normal volunteer with promising results.

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