Informatics Publishing Limited

M. Muthuselvi ¹, M. Malleswaran ²

Affiliations
1 Electrical and Electronics Engineering Department, Anna University of Technology Tirunelveli, Tirunelveli, IN
2 Electronics and Communication Engineering Department, Anna University of Technology Tirunelveli, Tirunelveli, IN

Abstract

This paper presents Neighborhood learning automata like (NLAL) mechanism for congestion avoidance in wired and wireless networks. The gateway and node detects incipient congestion by computing the average queue size. The gateway and node could notify connections of congestion by dropping packets arriving at the gateway and node. When the average queue size exceeds a preset threshold, the gateway drops or marks each arriving packet with a certain probability, where the exact probability is a function of queue size. Here Neighborhood Learning Automata Like Random Early Detection (NLALRED) is founded on the principles of the operations of existing LALRED congestion avoidance mechanisms along with a LAL philosophy. The primary objective of NLALRED is to optimize the value of the average size of the queue used for congestion avoidance and to consequently reduce the total loss of packets at the queue. The LAL scheme chooses the action that possesses the maximal ratio between the number of times the chosen action is rewarded and the number of times that it has been chosen. NLALRED minimizes the number of packet drops in wired & wireless networks. This approach helps to improve the performance of congestion avoidance & Throughput by adaptively minimizing the average queue size. Simulation results obtained using Network Simulator (NS2) establish the improved performance of NLALRED over the traditional RED methods. Simulations of a Transmission Control Protocol/Internet Protocol (TCP/IP) network are used to illustrate the performance of NLALRED gateway and nodes.

Keywords

IP, LA, LAL, LALRED, NLALRED, RED, TCP.

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M. Malleswaran ¹, V. Vaidehi ², J. Mary Anita ³

Affiliations
1 Electronics and Communication Engineering Department, Anna University of Technology Tirunelveli, Tirunelveli, 627007, IN
2 Information Technology Department, Anna University, MIT Campus, Chennai, IN
3 Electrical and Electronics Engineering Department, Anna University of Technology Tirunelveli, Tirunelveli, 627007, IN

Abstract

The GPS/INS integration is the adequate solution to provide a navigation system that has superior performance in comparison with either a GPS or an INS stand-alone system. The GPS/INS integration is typically carried out through Kalman filter (KF). However, the fact that KF highly depends on a predefined dynamics model forms a major drawback. Most recently, Adaptive neuro fuzzy inference system (ANFIS) has been proposed which is trained during the availability of GPS signal to map the error between the GPS and the INS. Then it will be used to predict the error of the INS position components during GPS signal blockage. This paper introduces a hybrid evolutionary algorithm of cooperative particle swarm optimization (CPSO) and cultural algorithm that is used to update the ANFIS parameters. The results demonstrate the comparison of the optimized ANFIS with PSO, CPSO and with hybrid evolutionary algorithm of cultural cooperative particle swarm optimization (CPSO) and cultural algorithm (CA), for INS/GPS integration.

Keywords

INS/GPS, ANFIS, Data Fusion, CPSO, CA.

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S. N. Sabreen ¹, M. Malleswaran ²

Affiliations
1 Electronics Engineering Department, Anna University of Technology Tirunelveli, Tirunelveli, 627007, IN
2 Electronics and Communication Engineering Department, Anna University of Technology Tirunelveli, Tirunelveli, 627007, IN

Abstract

A Recurrent Functional-Link (FL) based Fuzzy Neural Network (FNN) controller with Improved Particle Swarm Optimization (IPSO) is proposed in this paper to control a three phase Induction Generator (IG) system for stand-alone power application. Two online-trained recurrent FL-based FNNs are introduced as the regulating controllers for both the dc-link voltage of the ac/dc power converter and the ac line voltage of the dc/ac power inverter.

Keywords

Functional-Link Neural Network (FLNN), Induction Generator (IG), Improved Particle Swarm Optimization (IPSO).

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S. Manjula ¹, M. Malleswaran ²

Affiliations
1 Electrical and Electronics Engineering Department, Anna University of Technology Tirunelveli, Tirunelveli, 627007, IN
2 Electronics and Communication Engineering Department, Anna University of Technology Tirunelveli, Tirunelveli, IN

Abstract

A high order feed forward neural network architecture like Multiplicative Neural Network (MNN), Sigma-Pi Network (SPN) and Improved Sigma-Pi Network (ISPN) are used for adaptive channel equalization in this paper. The replacement of summation by multiplication at the neuron results in more powerful mapping because of its capability of processing higher-order information from training data. The equalizer is tested on Rayleigh fading channel with BPSK and QPSK signals. Performance comparison with back propagation (BPN) neural network shows that the proposed equalizer provides compact architecture and satisfactory results in terms of bit error rate performance at various levels of energy per power spectral density ratios for a Rayleigh fading channel, and also in terms of learning rate and training time.

Keywords

Channel Equalization, MNN, SPN, ISPN, BPN, Rayleigh Fading Channel.

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M. Malleswaran ¹, V. Vaidehi ², S. Angel Deborah ³

Affiliations
1 Electronics and Communication Engineering Department, Anna University of Technology Tirunelveli, Tirunelveli-627007, IN
2 Information Technology Department, Anna University, MIT Campus, Chennai, IN
3 Electrical and Electronics Engineering Department, Anna University of Technology Tirunelveli, Tirunelveli-627007, IN

Abstract

GPS and INS are commonly integrated using a Kalman filter (KF) to provide a robust aircraft navigation solution, overcoming drawbacks of GPS satellite signals blockage. This work presents an alternative method of integrating GPS and INS data, called Artificial Neural Networks. This method uses Adaptive Resonance Theory-Counter Propagation Neural Networks (ART-CPN) to predict the INS position error. The performance of ART-CPN is analyzed using real time data in terms of Root Mean Square Error (RMSE), Performance Index (PI), number of hidden neuron, number of epochs and learning rate. The performance of Forward only Counter Propagation Network (FCPN) and Full Counter Propagation Network (Full CPN) are also analyzed and compared with ART-CPN. ART-CPN is found to have better clustering ability when compared to FCPN and Full CPN. ART-CPN also has better learning ability and network constructing ability when compared to FCPN and Full CPN. It has better learning speed due to its one step learning process.

Keywords

ART-CPN, FCPN, Full CPN, GPS, INS, NPUA.

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J. Mary Anita ¹, M. Malleswaran ²

Affiliations
1 Electrical and Electronics Engineering Department, Anna University of Technology Tirunelveli, Tirunelveli-627007, IN
2 Electronics and Communication Engineering Department, Anna University of Technology Tirunelveli, Tirunelveli-627007, IN

Abstract

The next generation of wireless communication systems, called beyond third generation (B3G) or fourth generation (4G), will involve the integration of diverse but complementary cellular and wireless technologies, all of which will coexist in a heterogeneous wireless access environment. One of the major design issues in heterogeneous wireless networks is the support of vertical handoff, vertical handoff occurs when a mobile terminal switches from one network to another. More intelligent vertical handoff algorithms which consider user profiles, application requirements, and network conditions must be employed in order to provide enhanced performance results for both user and network. Handoff decision is the ability to decide when to perform the vertical handoff and determine the best handoff candidate access network. This paper provides vertical handoff decision algorithm that enables wireless access network selection at a mobile terminal using neuro fuzzy model and self evolving evolutionary learning. Our proposed vertical handoff decision algorithm is able to determine when a handoff is required, and selects the best access network that is optimized to network conditions, quality of service requirements, mobile terminal conditions, user preferences, and service cost. The results demonstrate the comparison of the optimized ANFIS with PSO, CPSO and with hybrid evolutionary algorithm of cultural cooperative particle swarm optimization (CPSO) and cultural algorithm (CA).

Keywords

CPSO, CA, ESE, SSE.

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