Asanga Ratnaweera

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This paper introduces a novel parameter automation strategy for the particle swarm algorithm and two further extensions to improve its performance after a predefined number of generations. Initially, to efficiently control the local search and convergence to the global optimum solution, time-varying acceleration coefficients (TVAC) are introduced in(More)
In this paper, a modified particle swarm optimisation algorithm is proposed for protein sequence motif discovery. Protein sequences are represented as a chain of symbols and a protein sequence motif is a short sequence that exists in most of the protein sequence families. Protein sequence symbols are converted into numbers using a one to one amino acid(More)
Rainfall is a complex process and forecasting rainfall is complicated because it involves a lot of parameters but precise rainfall forecasting is necessary for many human activities. Even though considerable research work has been done in rainfall prediction, comparatively fewer efforts have been made on Sri Lankan monsoon rainfall prediction using large(More)
A non linear relationship between an internal combustion engine and its engine parameters such as vibration signals/ exhaust gas is expected to be available. Under various fault conditions, vibration signals were collected using a test-bed to prove this. Fourier transformed vibration signals were mapped to their corresponding faults using a back propagation(More)
Artificial Neural Network (ANN) is a widely used technique in forecasting applications. An ensemble of ANNs can produce more accurate forecasts than a single ANN. The performance of the ensemble depends on its' member ANN. Member selection for an ensemble is a complicated task that need balancing conflicting conditions. This paper presents a method to(More)
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