Parkinson‟s Disease Classification using Various Advanced Neural Network Classifiers
S.Surya Devi1, M.Sivachitra2, P.Vadivel3
1S.Surya Devi, student of M.E., Applied Electronics, Department of EEE, Kongu Engineering College, Erode, Tamil Nadu, India.
2Dr.M. Sivachitra, Professor, Department of EEE, Kongu Engineering College, Erode, Tamil Nadu, India.
3Dr.P.Vadivel, Assistant Professor (Senior Grade), Department of Mathematics, Kongu Engineering College, Erode, Tamil Nadu, India.

Manuscript received on November 20, 2019. | Revised Manuscript received on November 26, 2019. | Manuscript published on 30 November, 2019. | PP: 3675-3679 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7924118419/2019©BEIESP | DOI: 10.35940/ijrte.D7924.118419

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Abstract: This paper proposes the application of Online Meta-neuron Based Learning Algorithm (OMLA), Self adaptive Resource Allocation Network (SRAN) and Projection Based Learning Meta-cognitive Radial Basis Functional Network (PBL-McRBFN) for Parkinson’s disease classification. This is the first journal paper to apply the concept of OMLA, SRAN and PBL-McRBFN for Parkinson’s disease classification. Online Meta-neuron based Learning Algorithm (OMLA) is a newly evolved network applied for Parkinson’s disease classification. This classifier make use of both global and local information of the network. Self Adaptive Resource Allocation Network (SRAN) consists of self adaptive control parameters that changes training data sequence, develop network architecture and learns network parameters. Also, repeated learning samples are removed with the help of this algorithm, hence training time and over flow problems can be minimized. The Projection Based Learning algorithm determines the output parameters of the network such that the energy function is minimum. The result shows the comparison of efficiency for the three networks in classification of Parkinson’s disease.
Keywords: Online Meta-Neuron, Projection Based Learning, Resource Allocation Network, Self Adaptive Control Parameters.
Scope of the Article: Classification.