A new recurrent neurofuzzy network for identification of dynamic systems
Fuzzy Sets and Systems
A fuzzy-neural multi-model for nonlinear systems identification and control
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
Dynamical membership functions: an approach for adaptive fuzzy modelling
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
Stable adaptive control with recurrent networks
Automatica (Journal of IFAC)
Neurocontrol of nonlinear systems via local memory neurons
Mathematical and Computer Modelling: An International Journal
Neurocontrol: A literature survey
Mathematical and Computer Modelling: An International Journal
Neural identification of dynamic systems on FPGA with improved PSO learning
Applied Soft Computing
A context layered locally recurrent neural network for dynamic system identification
Engineering Applications of Artificial Intelligence
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This paper discusses memory neuron networks as models for identification and adaptive control of nonlinear dynamical systems. These are a class of recurrent networks obtained by adding trainable temporal elements to feedforward networks that makes the output history-sensitive. By virtue of this capability, these networks can identify dynamical systems without having to be explicitly fed with past inputs and outputs. Thus, they can identify systems whose order is unknown or systems with unknown delay. It is argued that for satisfactory modeling of dynamical systems, neural networks should be endowed with such internal memory. The paper presents a preliminary analysis of the learning algorithm, providing theoretical justification for the identification method. Methods for adaptive control of nonlinear systems using these networks are presented. Through extensive simulations, these models are shown to be effective both for identification and model reference adaptive control of nonlinear systems