Systematic Study of Fuzzy, Neural Network and Neurofuzzy Systems
نویسندگان
چکیده
The techniques in artificial intelligence are used in almost all the fields where human reasoning and uncertainties can be effectively modeled. The popular techniques in AI are fuzzy logic and neural networks which can be used either separately or applied together. When they are used in combined way, they are called Neuro-Fuzzy Systems. The reasons to combine these two paradigms come out of the difficulties and inherit limitations of each isolated paradigm. This paper shows uniqueness in each technology and compares with others to show the need for hybrid system (Neuro fuzzy).It gives an insight of different concepts how they are utilized in various fields to yield optimized solution.
منابع مشابه
Financial credit-risk evaluation with neural and neurofuzzy systems
Credit-risk evaluation decisions are important for the ®nancial institutions involved due to the high level of risk associated with wrong decisions. The process of making credit-risk evaluation decision is complex and unstructured. Neural networks are known to perform reasonably well compared to alternate methods for this problem. However, a drawback of using neural networks for credit-risk eva...
متن کاملOnline Fault Diagnosis of Nonlinear Systems Based on Neurofuzzy Networks
Artificial intelligence techniques such as neural networks and fuzzy logic have been widely used in fault detection and diagnosis. Combining these two techniques, referred to as neurofuzzy networks, provides a powerful tool for modelling. B-spline neurofuzzy networks are used to model the residuals. The weights of the networks are trained online using recursive least squares method. Fuzzy rules...
متن کاملA Comparative Study of the Neural Network, Fuzzy Logic, and Nero-fuzzy Systems in Seismic Reservoir Characterization: An Example from Arab (Surmeh) Reservoir as an Iranian Gas Field, Persian Gulf Basin
Intelligent reservoir characterization using seismic attributes and hydraulic flow units has a vital role in the description of oil and gas traps. The predicted model allows an accurate understanding of the reservoir quality, especially at the un-cored well location. This study was conducted in two major steps. In the first step, the survey compared different intelligent techniques to discover ...
متن کاملA Neurofuzzy Learning and its Application to Control System
A neurofuzzy approach for a given set of input-output training data is proposed in two phases. Firstly, the data set is partitioned automatically into a set of clusters. Then a fuzzy if-then rule is extracted from each cluster to form a fuzzy rule base. Secondly, a fuzzy neural network is constructed accordingly and parameters are tuned to increase the precision of the fuzzy rule base. This net...
متن کاملA Recurrent Fuzzy Neural Network: Learning and Application
A novel recurrent neurofuzzy network is proposed in this paper. More specifically, in this work we generalize the recurrent neurofuzzy network structure proposed in [1], which is in turn is an improvement of the feedforward structure introduced in [2]. The network structure is composed by two structures: a fuzzy inference system and a neural network. The fuzzy inference system contains fuzzy ne...
متن کامل