... artificial neural networks with soft computing enables to construct learning ma- chines that are superior compared to classical artificial neural networks because knowledge can be extracted and explained in ...
... hybrid FuzzyNeuralNetwork (FNN) system for function approximation is ...and fuzzy inputs ...the network while the Fuzzy rule based knowledge is translated directly into ...
... the neuralnetwork to the classroom teaching quality ...the neuralnetwork in the teaching evaluation in terms of the idea and ...the neuralnetwork, this paper proposes a ...
... fully fuzzyneural networks with fuzzy connection weights is long computa- tion ...partially fuzzyneuralnetwork (PFNN) architec- ture where connection weights to output unit ...
... a fuzzy differential equation by using the strongly generalized differen- tiability ...of fuzzyneuralnetwork for the solution of differential equation with fuzzy initial value is ...
... the neuralnetwork and fuzzy logic ...However, fuzzy logic technique is more accurate but it fully depends on expert knowledge, which sometime may not always available [13, ...[11]. ...
... the network input layer and output layer are respectively composed of same M neurons, and neuron number K of the middle layer is smaller than M Provide the same learning mode in the input layer and output ...
... According to this article, the date which is used to analysis comes from the Case Western Reserve university provides in the network. Gather the asynchronous machine four condition’s signals as normal, rolling ...
... a fuzzyneuralnetwork model using a back- propagation learning ...suggested fuzzy-neuralnetwork models for supervised and unsupervised ...implements fuzzy inference ...
... Abstract: One of the broadly used internet attacks to deceive customers financially in banks and agencies is unknown “zero-day” phishing Emails “zero-day” phishing Emails is a new phishing email that it has not been ...
... neural networks, such as, multi-layer perceptron, radial basis function networks, and fuzzyneuralnetwork (FNN), etc. can be selected. In this paper, the FNN is used as a non-linear filter. A ...
... a fuzzyneuralnetwork for clustering and ...this fuzzyneuralnetwork two training algorithm are implemented for clustering and ...General Fuzzy min max Neural ...
... Unsupervised FuzzyNeuralNetwork (UFNN) of Kwan and Cai, and compose a new FNN with 6 types of fuzzy neurons and its associated self organizing supervised learning ...Supervised Fuzzy ...
... Based on this configuration, AGFINN model has been involved in forecasting the maximum (22h) and minimum (04h) price respectively. Best results were produced by including 20 fuzzy rules for the case of 22h, while ...
... the fuzzy delta learning rule such that the input-output relation of each unit was defined by the Zadeh extension principle ...feed-forward fuzzyneural networks to find a real solution of a ...
... The proliferation of computer networks increases the need for improved network management techniques. As computer networks expand, they become more complex as they attempt to support a more diverse selection of ...
... using fuzzy –neural ...using fuzzy clustering method. Then, by using fuzzy inference system, fuzzy rules based on related clusters and user’s interests are extracted and after acquiring ...
... artificial neuralnetwork (ANN) and fuzzy logic ...The neuralnetwork controller is employed to achieve the maximum power point (MPP) for different types of photo- voltaic (PV) ...
... The aforementioned research provided evidences of the estimation and/or prediction of evaporation only at gauged sites using ANNs. In this study, a hybrid computational model is proposed by combining the ANN and kriging ...
... In this research work, Functional Update Back Propagation Algorithm [13], Locally Weighted Projection Regression and Fuzzy Logic have been implemented. All the algorithms are able to train the lock status and ...