Siba Prasada Panigrahi

DEPSO and PSO-QI in digital filter design

This paper proposes two hybrid algorithms, one between particle swarm optimization (PSO) and differential evolution (DE) and second between PSO and quantum infusion (QI). This paper applies these algorithms for digital filter design. PSO algorithm is used as a basis for comparison. Extensive simulation results show the superiority of algorithms developed in this paper.

A novel algorithm for dynamic task scheduling

This paper deals with the problem of dynamic task scheduling in grid environment of multi-processors. First, this paper formulates task scheduling as an optimization problem and then optimizes with a novel hybrid optimization algorithm. The proposed algorithm combines the merits of Genetic Algorithm and Bacteria Foraging optimization. The simulation result proves the superior performance with the proposed algorithm.

Swarm intelligence based techniques for digital filter design

Two novel modifications to QPSO proposed.Time dependency of constriction factor taken care.Improved methods for IIR filter design proposed and validated. This paper deals with the problem of digital IIR filter design. Two novel modifications are proposed to Particle Swarm Optimization and validated through novel application for design of IIR filter. First modification is based on quantum mechanics and proved to yield a better performance. The second modification is to take care of time dependency character of the constriction factor. Extensive simulation results validate the superior performance of proposed algorithms.

Hybrid ANN reducing training time requirements and decision delay for equalization in presence of co-channel interference

Bayesian equalizer is known to be the optimum equalizer. This paper proposes a Hybrid Artificial Neural Network (Hybrid ANN) and an algorithm to modify Decision Feedback Equalizer (DFE) function of Bayesian equalizer while equalizing in presence of co-channel interference (CCI). A combination of Artificial Neural Network and Decision Feedback Equalizer (DFE) is termed as Neural-DFE (NDFE). The results show that the decision delay and training time requirement reduces significantly by use of NDFE. This creates an advantage specifically for a mobile environment where the CCI is varying in nature and the Bayesian equalizer requires a lot of training time.

A new training scheme for neural networks and application in non-linear channel equalization

A Recently proposed DSO trained ANN used for the problem of channel equalization.This paper introduced a novel strategy for equalization of nonlinear channels using this DSO trained neural network.Proposed method of channel equalization performs better than contemporary equalization methods used in the literature. This paper deals with the problem of equalization of channels in a digital communication system. In the literature, artificial neural network (ANN) has been increasingly used for the said problem. However, traditional methods of ANN training fall short of desired performance in the problem of equalization. In this paper, we propose a recently proposed training method for ANN for the problem. This training uses directed search optimization (DSO) as a trainer to neural networks. Then, we apply the same to the problem of nonlinear channel equalization and in that way, this paper introduces a novel strategy for equalization of nonlinear channels. Proposed method of channel equalization performs better than contemporary equalization methods used in the literature, as evident from extensive simulation results presented in this paper.

Non-linear channel equalization using adaptive MPNN

In this paper the Modified Probabilistic Neural Network (MPNN) is used for dealing with the problem of channel equalization. Some improvements are suggested for the MPNN so that it is more suitable for the current problem. Firstly, the MPNN is extended to process complex signals. Secondly, a stochastic gradient adaptation technique is proposed, such that when the network is being employed to equalize a slowly varying channel, it can self-adapt to the changing environment. Simulations have shown that the MPNN is able to effectively equalize 4-QAM symbol sequences transmitted through a non-linear, slowly time-varying channel. Finally, methods that further reduce the size of the network are proposed. Simulations show that the proposed method is able to reduce the size of the network considerably.

Efficient energy management strategies for hybrid electric vehicles using shuffled frog-leaping algorithm

This paper proposes two novel approaches for the problem of energy management in hybrid electric vehicles. Shuffled frog-leaping algorithm (SFLA) is a recently proposed population-based optimization algorithm. This paper first formulates energy management as an optimization problem and optimizes the problem using SFLA. Then the paper makes use of SFLA as a training algorithm to train artificial neural network (ANN) and this SFLA-trained ANN is used for energy management. Interestingly, the proposed approaches of this paper are found to be robust and more efficient than contemporary approaches.

Energy management in hybrid electric vehicles using optimized radial basis function neural network

This paper deals with energy management in hybrid electric vehicles. Use of radial basis function neural network (RBFNN) for the problem of energy management gains importance in the present decade. Use of genetic algorithm (GA) and particle swarm optimization (PSO) as optimization algorithms for parameter estimation is also well known. However, none of the researchers in the area tried to use GA and PSO as training algorithms for the problem. Hence in this paper, we propose two novel methods, based on RBFNN. The difference between RBFNN-based approaches in the literature and those used in this paper is the use of GA and PSO (i.e. optimising algorithms) as training algorithm to train RBFNNs. Interestingly, it is seen that the proposed approaches of this paper outperform RBFNN-based approaches in the literature with traditional training.

Reduced Complexity Dynamic Systems Using Approximate Control Moments

This paper deals with reduction of computational complexities in dynamic systems. This paper develops a novel method of reducing complexities with use of control moments of the system. Though the proposed method is validated through channel estimation in this paper, the same can be equally applied to any other dynamic systems. Encouraging results given in this paper prove that the computational complexities can be reduced up to 104 with a marginal affordable loss of performance.

An Efficient Noise Generator for Validation of Channels Equalizers

This paper develops an efficient pseudo-random number generator for validation of digital communication channels and secure disc. Drives. Simulation results validates the effectiveness of the random number generator.