Neural Networks, Qualitative-Fuzzy Logic and Granular Adaptive Systems
نویسنده
چکیده
Though traditional neural networks and fuzzy logic are powerful universal approximators, however without some refinements, they may not, in general, be good approximators for adaptive systems. By extending fuzzy sets to qualitative fuzzy sets, fuzzy logic may become universal approximators for adaptive systems. Similar considerations can be extended to neural networks.
منابع مشابه
INTEGRATED ADAPTIVE FUZZY CLUSTERING (IAFC) NEURAL NETWORKS USING FUZZY LEARNING RULES
The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...
متن کاملبهبود عملکرد سامانههای کنترل از طریق شبکه با استفاده از چرخش در قوانین کنترلگر منطق فازی
This paper addresses a novel control method adapted with varying time delay to improve NCS performance. A well-known challenge with NCSs is the stochastic time delay. Conventional controllers such as PID type controllers which are just tuned with a constant time delay could not be a solution for these systems. Fuzzy logic controllers due to their nonlinear characteristic which is compatible wit...
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 کاملModeling of streamflow- suspended sediment load relationship by adaptive neuro-fuzzy and artificial neural network approaches (Case study: Dalaki River, Iran)
Modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to itsessential application to water resources management. Recently, artificial intelligence has gained much popularity owing toits application in calibrating the nonlinear relationships inherent in the stream flow–suspended sediment relationship. Thisstudy made us of adaptive neuro-fuzzy ...
متن کاملAdaptive Modulation Using Neuro-Fuzzy (N-F) Controller for OFDM System
As demand for high quality transmission increases, improving spectrum efficiency and error performance in wireless communication systems are important. OFDM is a multi-carrier modulation technique with densely spaced sub-carriers that has gained a lot of popularity among the broadband community in the last few years. One of the promising approaches to next generation communication systems are a...
متن کامل