Titlebook: Neural Networks for Identification, Prediction and Control; Duc Truong Pham,Xing Liu Book 1995 Springer-Verlag London Limited 1995 backpro

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Decrepit

Neural Networks for Identification, Prediction and Control978-1-4471-3244-8

投票

獨(dú)白

Book 1995earning algorithms. The neural network types considered in detail are the muhilayer perceptron (MLP), the Elman and Jordan networks and the Group-Method-of-Data-Handling (GMDH) network. In addition, cerebellar-model-articulation-controller (CMAC) networks and neuromorphic fuzzy logic systems are als

要塞

orks and learning algorithms. The neural network types considered in detail are the muhilayer perceptron (MLP), the Elman and Jordan networks and the Group-Method-of-Data-Handling (GMDH) network. In addition, cerebellar-model-articulation-controller (CMAC) networks and neuromorphic fuzzy logic systems are als978-1-4471-3246-2978-1-4471-3244-8

昆蟲(chóng)

Dynamic System Identification Using Recurrent Neural Networks,lements are connected in such a way that all signals flow in one direction from input units to output units. In recurrent networks there are both feedforward and feedback connections along which signals can propagate in opposite directions.

陳腐思想

Modelling and Prediction Using GMDH Networks, identification methods include simplicity of implementation and good approximation properties [Warwick et aI, 1992]. In feedforward network based identification schemes, neural networks are used to represent the implied static mapping between the available input and output data. The network structu

Demulcent

百靈鳥(niǎo)

陰謀小團(tuán)體

Neuromorphic Fuzzy Controller Design,l network which can be trained using a Genetic Algorithm (GA). The GA is employed to determine the membership functions for the input variable, the quantisation levels of the output variable and the elements of the relation matrix of the FLC. The reasons for such a neuromorphic FLC are provided. The