A fuzzy neural network is an approach to mimic the structure and function of our brain. This method is widely applied in character recognition, but not in medical image recognition. The area of medical image recognition is challenging to our interest and can be maximally utilized the advantage of fuzzy neural networks in practice. In this paper we propose and new neural algorithm which is integrated both fuzzy self-organized and supervised learning methods. The proposed algorithm is applied for bronchogenic cancer diagnosis. The experimental results show that the correct recognition rate of our algorithm is superior to that of other neural networks.
Brain ; Diagnosis ; Fuzzy Logic ; Learning

OBJECTIVE: This study aimed to develop expert fuzzy logic model to assist physicians in the prediction of postoperative complications of prostatic hyperplasia before surgery. METHODS: A method for classification of surgical risks was developed. The effect of rotation of the current-voltage characteristics at biologically active points (acupuncture points) was used for the formation of classifier descriptors. The effect determined reversible and non-reversible changes in electrical resistance at acupuncture points with periodic exposure to a sawtooth probe current. Then, the developed method was tested on the prediction of the success of surgical treatment of benign prostatic hyperplasia. RESULTS: Input descriptors were obtained from collected data including current-voltage characteristics of 5 acupuncture points and composed of 27 arrays feeding in the model. The maximum diagnostic sensitivity of the classifier for the success of a surgical operation in the control sample was 88% and for testing data set prediction accuracy was 97%. CONCLUSION The use of tuples of current-voltage characteristic descriptors of acupuncture points in the classifiers could be used to predict the success of surgical treatment with satisfactory accuracy. The model can be a valuable tool to support physicians' diagnosis.
Acupuncture Points ; Acupuncture Therapy ; Fuzzy Logic

In order to develop safe training intensity and training methods for the passive balance rehabilitation train- ing system, we propose in this paper a mathematical model for human standing balance adjustment based on T-S fuzzy identification method. This model takes the acceleration of a multidimensional motion platform as its inputs, and human joint angles as its outputs. We used the artificial bee colony optimization algorithm to improve fuzzy C--means clustering algorithm, which enhanced the efficiency of the identification for antecedent parameters. Through some experiments, the data of 9 testees were collected, which were used for model training and model results validation. With the mean square error and cross-correlation between the simulation data and measured data, we concluded that the model was accurate and reasonable.
Algorithms ; Cluster Analysis ; Fuzzy Logic ; Humans ; Models, Theoretical ; Postural Balance

Cluster Analysis ; Fuzzy Logic ; Humans ; Mathematics ; Medicine, Chinese Traditional

For optimal adaptive cultivation region selection, we used ecology factors characterized Duolun region as model area to carry out the adaptive habitat division of Paeonia lactiflora. Similar priority comparison of ecology factors.in 91 cities were calculated by Fuzzy methods, then, distance of the ecology factors were transferred to spacial model by geography information system (,GIS) and modified by soil utilization map of China. The results showed that P. lactiflora were mainly distributed in the Daxing'an Mountain, Changbaishan and qinling range which were divided into six grades of suitable regions belonging to three geographical distributed units. The most similar areas to Duolun were Huade, Xilinhaote, Suolun and Zhangbei. P. lactiflora's distribution and quality are relevant with longitude and latitude, and temperature and rainfall.
China ; Environment ; Fuzzy Logic ; Paeonia ; classification ; growth & development ; Temperature

Fuzzy Logic ; Humans ; Occupational Diseases ; etiology ; Risk Assessment

This article provides demonstrations and calculations, using rough-set theory and method, of the math-morphological features (MMFs), such as form parameter, lobation and sphericity, etc. drawn from 28 species of insects of the Hemiptera, Lepidoptera and Coleoptera based on their images. The results are compared with statistical analysis made by Zhao Hanqing, and also with the traditional classifications through the pattern recognition of neural network on the basis of the rough-set disposal. The result of the experiments showed that when used in categorical taxonomy, the importance of MMF was ranked from high to low: (roundness-likelihood. eccentricity) > (hot-hole number, sphericity, circularity) > (lobation, form parameter). The results of pattern recognition by neural network were completely identical with those of traditional classifications. Accordingly, the conclusion was that this theory applied in insect taxonomy was more idealistic compared with statistical analysis method, and it had great significance when used with rough-set neural network.
Algorithms ; Animals ; Fuzzy Logic ; Insecta ; classification ; Neural Networks (Computer)

Risks exist in medicine related fields, which cannot be defined and quantified precisely. It is necessary to adopt a method for the risk assessment of uncertain and fuzzy phenomenon. This paper summarizes the thinking, procedure, advantage and application of fuzzy analytic hierarchy process in the risk assessment in medicine related fields for the purpose of providing reference for its further application.
Analytic Hierarchy Process ; Fuzzy Logic ; Humans ; Risk Assessment/methods*

BACKGROUND: Disease diagnosis is complicated since patients may demonstrate similar symptoms but physician may diagnose different diseases. There are a few number of investigations aimed to create a fuzzy expert system, as a computer aided system for disease diagnosis. METHODS: In this research, a cross-sectional descriptive study conducted in a kidney clinic in Tehran, Iran in 2012. Medical diagnosis fuzzy rules applied, and a set of symptoms related to the set of considered diseases defined. The input case to be diagnosed defined by assigning a fuzzy value to each symptom and then three physicians asked about each suspected diseases. Then comments of those three physicians summarized for each disease. The fuzzy inference applied to obtain a decision fuzzy set for each disease, and crisp decision values attained to determine the certainty of existence for each disease. RESULTS: Results indicated that, in the diagnosis of seven cases of kidney disease by examining 21 indicators using fuzzy expert system, kidney stone disease with 63% certainty was the most probable, renal tubular was at the lowest level with 15%, and other kidney diseases were at the other levels. The most remarkable finding of this study was that results of kidney disease diagnosis (e.g., kidney stone) via fuzzy expert system were fully compatible with those of kidney physicians. CONCLUSION: The proposed fuzzy expert system is a valid, reliable, and flexible instrument to diagnose several typical input cases. The developed system decreases the effort of initial physical checking and manual feeding of input symptoms.
Diagnosis ; Expert Systems ; Fuzzy Logic ; Humans ; Iran ; Kidney Calculi ; Kidney Diseases* ; Kidney*

The purpose of this paper is to report the research and design of control system of magnetic coupling centrifugal blood pump in our laboratory, and to briefly describe the structure of the magnetic coupling centrifugal blood pump and principles of the body circulation model. The performance of blood pump is not only related to materials and structure, but also depends on the control algorithm. We studied the algorithm about motor current double-loop control for brushless DC motor. In order to make the algorithm adjust parameter change in different situations, we used the self-tuning fuzzy PI control algorithm and gave the details about how to design fuzzy rules. We mainly used Matlab Simulink to simulate the motor control system to test the performance of algorithm, and briefly introduced how to implement these algorithms in hardware system. Finally, by building the platform and conducting experiments, we proved that self-tuning fuzzy PI control algorithm could greatly improve both dynamic and static performance of blood pump and make the motor speed and the blood pump flow stable and adjustable.
Algorithms ; Assisted Circulation ; instrumentation ; Fuzzy Logic ; Heart-Assist Devices ; Magnetic Phenomena ; Magnetics