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Preface. Notation. 1. Introduction. 2. Artificial Neural Networks: Architectures and Learning Rules. 3. Nonlinear System Identification Using Neural Networks. 4. Neural Networks for Control. 5. NLq Theory. 6. General Conclusions and Future Work. A: Generation of n-double scrolls. B: Fokker-Planck Learning Machine for Global Optimization. C: Proof of NLq Theorems. Bibliography. Index.
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Add Artificial Neural Networks for Modelling and Control of Non-Linear Systems, Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network , Artificial Neural Networks for Modelling and Control of Non-Linear Systems to the inventory that you are selling on WonderClubX
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Add Artificial Neural Networks for Modelling and Control of Non-Linear Systems, Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network , Artificial Neural Networks for Modelling and Control of Non-Linear Systems to your collection on WonderClub |