Norhamreeza Abdul Hamid

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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)
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)
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)
This paper presents a Metahybrid algorithm that consists of the dual combination of Wolf Search (WS) and Elman Recurrent Neural Network (ERNN). ERNN is one of the most efficient feed forward neural network learning algorithm. Since ERNN uses gradient descent technique during the training process; therefore, it is not devoid of local minima and slow(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)
Optimization of a circuit by transistor sizing is often a slow, tedious and iterative manual process which relies on designer intuition. It is highly desirable to automate the transistor sizing process towards being able to rapidly design high performance integrated circuit. Presented here is a simple but effective algorithm for automatically optimizing the(More)