Fault diagnosis and failure prognosis are essential techniques in improving the safety of many manufacturing systems. Therefore, on-line fault detection and isolation is one of the most important tasks in safety-critical and intelligent control systems. Computational intelligence techniques are being investigated as extension of the traditional fault diagnosis methods. This paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within an application study of a manufacturing system. The key issues of finding a suitable structure for detecting and isolating ten realistic actuator faults are described. Within this framework, data-processing interactive software of simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. This software devoted primarily to creation, training and test of a classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can be represented like a special type of fuzzy perceptron, with three layers used to classify patterns and failures. The system selected is the workshop of SCIMAT clinker, cement factory in Algeria.
In this study, a hybrid method based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) for diagnosing Liver disorders (ANFIS-PSO) is introduced. This smart diagnosis method deals with a combination of making an inference system and optimization process which tries to tune the hyper-parameters of ANFIS based on the data-set. The Liver diseases characteristics are taken from the UCI Repository of Machine Learning Databases. The number of these characteristic attributes are 7, and the sample number is 354. The right diagnosis performance of the ANFIS-PSO intelligent medical system for liver disease is evaluated by using classification accuracy, sensitivity and specificity analysis, respectively. According to the experimental results, the performance of ANFIS-PSO can be more considerable than traditional FIS and ANFIS without optimization phase.
The paper presents a comparison of various soft computing techniques used for filtering and enhancing speech signals. The three major techniques that fall under soft computing are neural networks, fuzzy systems and genetic algorithms. Other hybrid techniques such as neuro-fuzzy systems are also available. In general, soft computing techniques have been experimentally observed to give far superior performance as compared to non-soft computing techniques in terms of robustness and accuracy.
For the past few decades, man has been trying to create an intelligent computer which can talk and respond like he can. The task of creating a system that can talk like a human being is the primary objective of Automatic Speech Recognition. Various Speech Recognition techniques have been developed in theory and have been applied in practice. This paper discusses the problems that have been encountered in developing Speech Recognition, the techniques that have been applied to automate the task, and a representation of the core problems of present day Speech Recognition by using Fuzzy Mathematics.
A framework of new unified neural and neuro-fuzzy approaches for integrating implicit and explicit knowledge in neuro-symbolic systems is proposed. In the developed hybrid system, training data set is used for building neurofuzzy modules, and represents implicit domain knowledge. On the other hand, the explicit domain knowledge is represented by fuzzy rules, which are directly mapped into equivalent neural structures. Three methods to combine the explicit and implicit knowledge modules are proposed.