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Several non-linear systems show complex behaviors. For example, some of those plants present a high degree of oscillations throughout the time. Adaptive algorithms used to approximate such difficult neural behaviors can show important definciencies. The differential network is not an exception. Indeed, when just one neural network is applied to get an adequate approximation, the identification error...
Schrödinger equation is a well known example of the so-called complex partial differential equations (C-PDE). This paper presents a technique based in the Differential Neural Networks (DNN) methodology to solve the nonparametric identification problem of systems described by C.PDE. In this case, the identification scheme is proposed as the composition of two coupled DNN: the first one is used to...
In this paper, reduced-order synchronization of uncertain chaotic neuron models with different orders is investigated. The identifier and controller modules are designed completely independently. A modified recursive least square algorithm is used to identify the unknown parameters of the slave system, and the control module is designed based on optimal control strategy. A performance index is introduced,...
The adaptive linearization of dynamic nonlinear systems remains as an open problem due to the complexities associated with the methods required to obtain the linearized sections. This problem is even more difficult if the system is uncertain, it means, if only partial or null information about the mathematical model of the system is available. This paper presents a proposal of an adaptive linearization...
The paper proposed a neural network solution to the indirect vector control of three phase induction motor including a real-time trained neural controller for the IM angular velocity which permitted the speed up reaction to the variable load. The basic equations and elements of the indirect field oriented control scheme are given. The control scheme is realized by one recurrent and two feedforward...
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