In this study, an artificial neural network (ANN) based model for prediction of solar energy potential in Kuala Lumpur, Malaysia was developed. Standard multilayered, feed-forward, back-propagation neural networks were designed using Microsoft Excel (MS Excel). The meteorological data were acquired from Malaysia Meteorological Department. The data was consists of meteorological data from one st...

In this work, generalization ability of a hybrid neural network algorithm is investigated. This algorithm consists of a combination of Radial Basis Function (RBF) and Multilayer Perceptron (MLP) in one single network using conic section functions. The network architecture using this algorithm is called Conic Section Function Neural Network (CSFNN). Various problems are examined to demonstrate t...

The application of artificial neural network to compressor performance map prediction is investigated. Different types of artificial neural networks such as general regression neural network, rotated general regression neural network proposed by the authors, radial basis function network, and multilayer perceptron network are considered. Two different models are utilized in simulating the perfo...

In order to predict the ice accretion on overhead line conductors, five artificial neural network (ANN) architectures were explored and compared. Two static networks, Multilayer Perceptron and Radial Basis Functions, as well as two time dependent networks, Finite Impulse Response and Elman, were compared with multiple linear regression (ADALINE). Results indicated that the FIR network yielded t...

conclusions results showed that ann is a powerful tool for predicting sorption coefficients using soil organic carbon content variations. results the multilayer perceptron (mlp) artificial neural networks (ann) model with 1-6-1 layout, predicted nearly 98% of the variance of kd as well as 94% of the koc variations with soil organic carbon content. materials and methods data of this study were d...

Training neural networks is a complex task that is important for supervised learning. A few metaheuristic optimization techniques have been applied to increase the effectiveness of the training process. The Cuckoo Search (CS) algorithm is a recently developed meta-heuristic optimization algorithm which is suitable for solving optimization problems. In this paper, Cuckoo search is implemented in...

In our previous work we proposed a Multilayer Perceptron Neural Networks (MLP NN) consisting of fuzzy flipflops (F3) based on various operations. We showed that such kind of fuzzy-neural network had good learning properties. In this paper we propose an evolutionary approach for optimizing fuzzy flip-flop networks (FNN). Various popular fuzzy operation and three different fuzzy flip-flop types w...

Artificial neural networks (ANN), esp. multilayer perceptrons (MLP) have been widely used in pattern recognition and classification. Nevertheless, how to incorporate a priori knowledge to design ANN is still an open problem. This paper tries to give some insightful discussions on this topic emphasizing weight initialization from three perspectives. Theoretical analyses and simulations are offer...

this study shows the usefulness of artificial neural network (ann) in maintenance planning and man-agement. an ann model based on the multi-layer perceptron having three hidden layers and four processing elements per layer was built to predict the expected downtime resulting from a breakdown or a maintenance activity. the model achieved an accuracy of over 70% in predicting the expected downtime.

A multilayer perceptron is a feed forward artificial neural network model that maps sets of input data onto a set of appropriate output. It is a modification of the standard linear perceptron in that it uses three or more layers of neurons (nodes) with nonlinear activation functions and is more powerful than the perceptron in that it can distinguish data that is not linearly separable, or separ...