K. K. Aggarwal

Fuzzy controller for rapid nickel-cadmium batteries charger through adaptive neuro-fuzzy inference system (ANFIS) architecture

ANFIS architecture is a class of adaptive networks, which is functionally equivalent to fuzzy inference systems. The architecture has been employed for fuzzy modeling that learns information about a data-set in order to compute the membership functions and rule-base that best follow the given input-output data. ANFIS employs hybrid learning that combines the gradient method and the least squares estimates to identify premise and consequent parameters respectively. In this paper the fuzzy controller for rapidly charging nickel-cadmium (Ni-Cd) batteries charger has been designed through ANFIS. The behavior of Ni-Cd batteries was not known for higher charging rates and the input-output data of batteries has been obtained through rigorous experimentation with an objective to charge the batteries as quickly as possible, but without doing any damage to them. Takagi-Sugeno-Kang (TSK) model has been considered for the controller.

A Matlab Implementation of Swarm Intelligence based Methodology for Identification of Optimized Fuzzy Models

This chapter presents a Matlab toolbox viz. PSO Fuzzy Modeler for Matlab. The toolbox implements the fuzzy model identification procedure using PSO as an optimization engine, which was presented in the previous chapter. This toolbox provides the features to generate Mamdani and Singleton fuzzy models from the available data. The simulation results presented in the previous chapter have been obtained through this toolbox, which is freely distributed on SourceForge.net. SourceForge.net is the world’s largest development and download repository of open-source code and applications. This toolbox can serve as a valuable reference to the swarm intelligence community and others and help them in designing fuzzy models for their respective applications quickly.

Hysteresis plus timer based handover initiation algorithm for microcellular systems

This paper presents a new handover initiation algorithm based on relative signal strength measurements without averaging, and a timer. The performance metrics used are average number of handovers per cell boundary crossing, and handover initiation delay. Both of these, need to be minimized. Proposed algorithm was compared with the algorithms based on both hysteresis margin, and averaging of signal strength measurements. Our results show that the new algorithm yields the same performance as that obtained through the latter. Since the algorithm does not use averaging of the signal strength measurements, its implementation in real mobile systems is quite easy, which proves the usefulness of the proposed algorithm. Moreover, averaging always makes the handover process sluggish. The present algorithm does not use signal strength averaging. This fact yields a fast handover initiation algorithm, making it particularly suitable for implementation in microcellular systems.

Handover initiation control techniques in mobile cellular systems

This paper presents the handover process in terms of radio propagation issues. It covers the propagation phenomenon in mobile radio communications and fundamentals of handover process. It reviews different approaches and criteria based on received signal strength to detect the need for a handover. Recently developed initiation control techniques based on radio link measurements are studied. Adaptive and multicriteria handover algorithms are introduced. Mathematical models for performance evaluation of handover initiation control techniques are critically analyzed. Finally, key issues and future challenges in context with third generation wireless systems have been identified.

A New Empirical Formula for Handover Rate in Microcellular Systems

In this paper, we propose a new empirical formula for handover rate as a function of base stations separation, standard deviation of shadow fading, path loss exponent, averaging distance, and correlation distance. The handover initiation algorithm is based on averaged signal strength measurements using relative signal strength with hysteresis margin approach. We generate the data through computer simulations for the average number of handovers referred to as handover rate, for the practical range of path loss exponent and standard deviation of shadow fading. The proposed formula provides for a practical design tool to optimize the handover initiation performance under varied propagation environments.

Fuzzy Model Identification for Rapid Nickel-Cadmium Battery Charger through Particle Swarm Optimization Algorithm

This paper presents the fuzzy model identification for rapid Nickel-Cadmium (Ni-Cd) battery charger by applying Particle Swarm Optimization (PSO) algorithm on the input-output data. Models generated through this approach provide the flexibility of black-box approach like neural networks, since it does not need to know any information regarding the process that generates the data. The PSO method is a member of the broad category of swarm intelligence techniques for finding optimized solutions. The motivation behind the PSO algorithm is the social behavior of animals viz. flocking of birds and fish schooling and has its origin in simulation for visualizing the synchronized choreography of bird flock. The data for the batteries charger was obtained through experimentation with an objective to charge the batteries as fast as possible. The implementation of the approach is described and simulation results are presented to illustrate its effectiveness.

Special Tutorial — Particle Swarms for Fuzzy Models Identification

The problem of fuzzy system modeling or fuzzy model identification is generally the determination of a fuzzy model for a system or process by making use of linguistic information obtained from human experts and/or numerical information obtained from input-output numerical measurements. The former approach is known as knowledge-driven modeling while the later is known as data-driven modeling. It is also possible to integrate the two approaches for developing models of complex real systems. In this tutorial, attention is focused on building optimized fuzzy model from the available data based on relatively new identification technique viz. particle swarm optimization (PSO).

Analytical Performance Evaluation of Inter-System Handover

In this paper, we analyze the behavior of inter-system handover between second generation (GSM) and third generation (UMTS) mobile radio systems. A mathematical model is proposed to evaluate the handover performance. The handover initiation algorithm is based on the absolute signal strength thresholds. The algorithm performs handover when the averaged signal strength from the serving base station drops below a given threshold and that from the candidate base station exceeds a preset threshold. Handover rate and handover initiation delay are used as performance metrics. Based on numerical results, threshold parameter settings are determined for optimum handover performance.

Sensitivity of Handover Performance to Path Loss Exponent: A Variable Hysteresis Margin based Handover Initiation Algorithm

In this paper, we analyze the sensitivity of handover initiation algorithm to path loss exponent. The effect of the propagating model parameters on both the mean number of handovers, and handover initiation delay are investigated. The algorithm is based on signal strength measurements using relative signal strength with hysteresis margin approach. The mean number of handovers is estimated for a single trip of mobile station from one base station to another, henceforth referred to as handover rate. Different path loss exponents varying from 2 to 8 are used for the simulation and the corresponding impact on handover rate and delay was studied. It is observed that handover rate depends on the path loss exponent. In a nonuniform environment, path loss parameters may be different and a given handover scheme cannot give optimum results consistently in the entire cellular network. A variable hysteresis margin based scheme for optimum handover performance under different propagation environments is proposed. The simulation results yield optimized tradeoff between handover rate and handover initiation delay.

Delay related reliability: A new performance index for computer communication network dynamics

Abstract As the traffic increases beyond certain threshold or the link failures take place the performance of a packet switched computer communication network degrades. Reliability and the mean packet delay are integrated to form a delay related reliability performance measure of the network dynamics. To quantify the performance degradation the mathematical formula has been developed to indicate instantaneous performance/ degradation.