This paper presents a fuzzy system that recognizes learning styles and emotions using two different neural networks. The first neural network (a Kohonen neural network) recognizes the student cognitive style. The second neural network (a back-propagation neural network) was used to recognize the student emotion. Both neural networks are being part of a fuzzy system used into an intelligent tuto...

Weather elements are the most important parameters in metrological and hydrological studies especially in semi-arid regions, like Jordan. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is used here to predict the minimum and maximum temperature of rainfall for the next 10 years using 30 years’ time series data for the period from 1985 to 2015. Several models were used based on different memb...

in this study two intelligent systems, based on adaptive neuro-fuzzy inference systems (anfis) and artificial neural networks (anns) of forecasting municipal solid wastes (msw) generation has been proposed. anfis and anns as an intelligent tool compared with together was used to monthly prediction of msw generated in tehran. monthly amount of solid wastes (sw), total monthly precipitation, mont...

Today, researchers have at their disposal, the required hardware, software, and sensor technologies to build intelligent autonomous systems. More, they are also in possession of some computational tool such as Fuzzy Logic(FL), Neural Networks (NN), Expert system(ES), Genetic Algorithms (GA) and other more technologies that are more effective in the design and development of intelligent autonomo...

The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on the one hand and the customer’s option to choose from several alternatives, the business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data ...

The ANFIS is the product of two methods, neural networks, and fuzzy systems. If both these intelligent methods are combined, better reasoning will be obtained in term of quality and quantity. In other words, both fuzzy reasoning and neural network calculation will be available simultaneously [7]. This ANFIS technique has been successfully applied by many researchers for sensor-based autonomous ...

Lithofacies identi®cation supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identi®cation from core data are costly and di€erent geologists may provide di€erent interpretations. In this paper, we present a lowcost intelligent system consisting of three adaptiv...

Several models have been created for Smart Grid resource-allocation problem. The principal purpose of the models is to connect power sources with appropriate sinks when considering the input parameters of power balance and consumption size, etc. Fuzzy logic is representative of these models. When creating the fuzzy model, the parameters and rule construction play the most significant role. For ...

Abst rac t The slip power recovery configuration is an attractive scheme of variable speed drive, with high efficiency and low converter rating; however, high performance control has being difficult. In this paper, novel applications of fuzzy logic for the intelligent control of a slip power recovery system are presented. A direct fuzzy logic controller and an adaptive fuzzy controller, based o...

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 ...