I. S. Baruch

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The paper proposed to apply a fuzzy-neural recurrent multi-model for systems identification and states estimation of complex nonlinear plants. The parameters of the local recurrent neural network models are used for a local indirect adaptive trajectory tracking control systems design. The designed local control laws are coordinated by a fuzzy rule based(More)
This paper proposes the use of a recurrent neural network model (RNNM) for decentralized and centralized identification of an aerobic digestion process, carried out in a fixed bed and a recirculation tank anaerobic wastewater treatment system. The analytical model of the digestion bioprocess represented a distributed parameter system, which is reduced to a(More)
The paper proposed to use Fuzzy-Neural Multi-Model (FNMM) identification and control system for decentralized control of distributed parameter anaerobic wastewater treatment digestion bioprocess, carried out in a fixed bed and a recirculation tank. The distributed parameter analytical model of the digestion bioprocess is reduced to a lumped system using the(More)
This paper proposes using a new recurrent neural network model (RNNM) to predict and control fed batch fermentations of Bacillus thuringiensis. The control variables are the limiting substrate and the feeding conditions. The multi-input multi-output RNNM proposed has twelve inputs, seven outputs, nineteen neurons in the hidden layer, and global and local(More)
In this paper we propose the use of a hybrid PSO-GA optimization method for automatic design of fuzzy logic controllers (FLC). The optimal fuzzy logic controllers are used for the trajectory tracking control of autonomous mobile robots. The bio-inspired and the evolutionary methods are used to find the parameters of the membership functions of the FLC to(More)
The paper proposed a new fuzzy-neural recurrent multi-model for systems identification and states estimation of complex nonlinear mechanical plants with backlash. The parameters and states of the local recurrent neural network models are used for a local direct and indirect adaptive control systems design. The designed local control laws are coordinated by(More)
An identification and direct adaptive neural control system with and without integral term is proposed. The system contains a neural identifier, and a neural controller, based on the recurrent trainable neural network model. The applicability of the proposed direct adaptive neural control system of both proportional and integral-term direct adaptive neural(More)
The paper proposed to use a recurrent neural network model, and a real-time Levenberg-Marquardt algorithm of its learning for centralized modeling, identification and I-term control of an anaerobic digestion bioprocess, carried out in a fixed bed and a recirculation tank of a wastewater treatment system. The analytical model of the digestion bioprocess,(More)
An improved universal parallel recurrent neural network canonical architecture, named Recurrent Trainable Neural Network (RTNN), suited for state-space systems identification, and an improved dynamic back-propagation method of its leaming, are proposed. The proposed R T " is studied with various representative examples and the results of its learning are(More)
This paper is devoted to the development of a Neural Network Hybrid Identification Framework for unknown Nonlinear Hybrid Dynamical Systems. The proposal is based in the well known Recurrent Trainable Neural Networks Identifiers. In a first instance, the unknown hybrid system is considered like a black-box where by using only hybrid input-output data an(More)