Norhamreeza Abdul Hamid

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The back propagation (BP) algorithm is a very popular learning approach in multilayer feedforward networks. However, the most serious problems associated with the BP are local minima problem and slow convergence speeds. Over the years, many improvements and modifications of the BP learning algorithm have been reported. In this research, we propose a new(More)
In some practical Neural Network (NN) applications, fast response to external events within enormously short time is highly demanded. However, by using back propagation (BP) based on gradient descent optimisation method obviously not satisfy in several application due to serious problems associated with BP which are slow learning convergence velocity and(More)
This paper presents the application of a combined approach of Higher Order Neural Networks and Recurrent Neural Networks, so called Jordan Pi-Sigma Neural Network (JPSN) for comprehensive temperature forecasting. In the present study, one-step-ahead forecasts are made for daily temperature measurement, by using a 5-year historical temperature measurement(More)
We proposed a method for improving the performance of the back propagation algorithm by introducing the adaptive gain of the activation function. In a 'feed forward' algorithm, the slope of the activation function is directly influenced by a parameter referred to as 'gain'. In this paper, the influence of the adaptive gain on the learning ability of a(More)