M.E. Bordon

A modified Hopfield model for solving the N-Queens problem

A neural network model for solving the N-Queens problem is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. The network is shown to be completely stable and globally convergent to the solutions of the N-Queens problem. Simulation results are presented to validate the proposed approach.

Evaluation and identification of lightning models by artificial neural networks

This paper describes a novel approach for mapping lightning models using artificial neural networks. The networks acts as identifier of structural features of the lightning models so that output parameters can be estimated and generalised from an input parameter set. Simulation examples are presented to validate the proposed approach. More specifically, the neural networks are used to compute electrical field intensity and critical disruptive voltage taking into account several atmospheric and structural factors, such as pressure, temperature, humidity, distance between phases, height of bus bars, and wave forms. A comparative analysis with other approaches is also provided to illustrate this new methodology.

Design of digital PID controller with gain planning based on CMAC

Presents the development of a proportional integral derivative (PID) digital controller with gain planning based on the (CMAC) cerebellar model articulation controller. This digital controller operates in closed loop and has a dynamic gain adjustment. The control strategy uses an algorithm that calculates the proportional, integral and derivative parcels. This algorithm provides the soft start requirements. Both, the gain planning and the soft start requirements, uses auxiliary variables to determine an appropriate and dynamic setup of the digital controller. These auxiliary variables impose several restrictions on the digital controller and this one must have adaptive characteristics. The artificial neural network is used to estimate these auxiliary variables. The learning process depends on the data acquisition or the mathematical model simulations. The digital controller can operate in real-time conditions and the sample frequency can reach 10 kHz.

Design and analysis of neural networks for systems optimization

Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements that are shown to be extremely effective in computation. This paper presents an architecture of artificial neural networks that can be used to solve several classes of optimization problems. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. Among the problems that can be treated by the proposed approach include combinational optimization problems and dynamic programming problems.

Identificação de Processos de Ensaios de Alta Tensão Através de Redes Neurais Artificiais

This paper demonstrates that artificial neural networks can be used effectively for the identification and estimation of parameters related to analysis and design of high-voltage substations. More specifically, the neural networks are used to compute electrical field intensity and critical disruptive voltage in substations taking into account several atmospheric and structural factors, such as pressure, temperature, humidity, distance between phases, height of bus bars, and wave forms. Simulation examples of tests are presented to validate the proposed approach. The results that were obtained by experimental evidences and numerical simulations allowed the proposition of new rules about the specification of substations.

Design of a neurofuzzy controller with simplified architecture.

This work presents the design of a neurofuzzy controller with simplified architecture that minimizes the processing time used in several stages associated with systems and processes modelling. The basic procedures of fuzzification and defuzzification are very simplified, whereas the inference procedures are computed in a direct way. The simplified architecture has allowed a fast and easy configuration of the neurofuzzy controller, as consequence, the control rules that define the control actions are obtained automatically. To validate the proposed approach, this neurofuzzy system is used in an industrial application for fluid flow control.

A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems.

A neural network model for solving constrained nonlinear optimization problems with bounded variables is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. The network is shown to be completely stable and globally convergent to the solutions of constrained nonlinear optimization problems. A fuzzy logic controller is incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.

Artificial neural networks applied in study of atmospheric parameters to high voltage substations concerning lightning

This paper demonstrates that artificial neural networks can be used effectively for estimation of parameters related to study of atmospheric conditions in high voltage substation design. Specifically, the neural networks are used to compute the variation of electrical field intensity and critical disruptive voltage in substations taking into account several atmospheric factors, such as pressure, temperature, humidity, so on. Examples of simulation of tests are presented to validate the proposed approach. The results that were obtained by experimental evidences and numerical simulations allowed the verification of the influence of atmospheric conditions on design of substations concerning lightning.

Simplified architecture of a neurofuzzy controller applied on industrial systems for fluid flow control

Presents a simplified architecture of a neurofuzzy controller for general purpose applications that tries to minimize the processing used in the several stages of hazy modeling of systems. The basic procedures of fuzzification and defuzzification are simplified to the maximum while the inference procedures are computed in a private way. The simplified architecture allows a fast and easy configuration of the neurofuzzy controller and the structuring rules that define the control actions is automatic. The controllers limits and performance are standardized and the control actions are previously calculated. For application, industrial systems of fluid flow control are considered.

A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems

A neural network model for solving constrained nonlinear optimization problems with bounded variables is presented. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. The network is shown to be completely stable and globally convergent to the solutions of constrained nonlinear optimization problems. A fuzzy logic controller is incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.