Quickprop

Abstract: The goal of this research is to develop an efficient SNN model for epilepsy and epileptic seizure detection using electroencephalograms (EEGs), a complicated pattern recognition problem Three training algorithms are investigated: SpikeProp (using both incremental and batch processing), QuickProp, and RProp Since the epilepsy and epileptic seizure detection problem requires a large training dataset the efficacy of these algorithms is investigated by first applying them to the XOR and Fisher iris benchmark problems Three measures of performance are investigated: number of convergence epochs, computational efficiency, and classification accuracy Extensive parametric analysis is performed to identify heuristic rules and optimum parameter values that increase the computational efficiency and classification accuracy The result is a remarkable increase in computational efficiency For the XOR problem, the computational efficiency of SpikeProp, QuickProp, and RProp is increased by a factor of 588, 82, and 75, respectively, compared with the results reported in the literature EEGs from three different subject groups are analyzed: (a) healthy subjects, (b) epileptic subjects during a seizure-free interval, and (c) epileptic subjects during a seizure It is concluded that RProp is the best training algorithm because it has the highest classification accuracy among all training algorithms specially for large size training datasets with about the same computational efficiency provided by SpikeProp The SNN model for EEG classification and epilepsy and seizure detection uses RProp as training algorithm This model yields a high classification accuracy of 925%

Abstract: In this paper we develop and analyze Spiking Neural Network (SNN) versions of Resilient Propagation (RProp) and QuickProp, both training methods used to speed up training in Artificial Neural Networks (ANNs) by making certain assumptions about the data and the error surface. Modifications are made to both algorithms to adapt them to SNNs. Results generated on standard XOR and Fisher Iris data sets using the QuickProp and RProp versions of SpikeProp are shown to converge to a final error of 0.5 - an average of 80% faster than using SpikeProp on its own.

Abstract: This paper proposes a neural network solution methodology for the problem of real power transfer capability calculations. Based on the optimal power flow formulation of the problem, the inputs, for the neural network are generator status, line status and load status and the output is the transfer capability. The Quickprop algorithm is used in the paper to train the neural network. A case study of the IEEE 30-bus system is presented demonstrating the feasibility of this approach. The new method will be useful for reliability assessment in the new utility environment.