Back-propagation Neural Network (BPNN) algorithm is one of the most widely used and a popular technique to optimize the feed forward neural network training. Traditional BP algorithm has some drawbacks, such as getting stuck easily in local minima and slow speed of convergence. Nature inspired meta-heuristic algorithms provide derivative-free solution to optimize complex problems. This paper pr...

Smart sensor is information detection, information processing, information memory, logical thinking and judging function of sensor. It not only has the various functions of the traditional sensor, but also has the data processing, fault diagnosis, non linear processing, self correction and man-machine communication. BP (Back Propagation) neural network is a kind of error back propagation traini...

This paper presents the results of a study aimed at estimating groundwater pollution source location from observed breakthrough curves using neural networks. Two different methods of presenting the breakthrough curves to the ANN are investigated. The feed-forward multi-layer perceptron (MLP) type artificial neural network (ANN) models are employed. The ANNs were trained using the back-propagati...

In this paper, novel techniques in increasing the accuracy and speed of convergence of a Feed forward Back propagation Artificial Neural Network (FFBPNN) with polynomial activation function reported in literature is presented. These technique was subsequently used to determine the coefficients of Autoregressive Moving Average (ARMA) and Autoregressive (AR) system. The results obtained by introd...

We consider the problem of learning from examples in layered linear feed-forward neural networks using optimization methods, such as back propagation, with respect to the usual quadratic error function E of the connection weights. Our main result is a complete description of the landscape attached to E in terms of principal component analysis. We show that E has a unique minimum corresponding t...

Abstract— To expand ontology meanings, an effective ontology mapping approach is needed to map related or similar knowledge from heterogeneous sources together. Especially, the mapping approach also can be applied to support image recognition in order to enhance its retrieval information. In this paper, we propose the ontology mapping with back propagation method to learn image objects, and lin...

Two schemes to obtain phonemic transcriptions of spoken utterances are described and compared. Both schemes utilize the so called Self-Organizing Kohonen Maps first to vector quantize speech into a sequence of phoneme Iabels centisecond apart. In the original scheme, this quasiphoneme sequence is converted into a phoneme string with simple durational transformation rules. In the scheme introduc...

Data processing and management is common now a days. In this paper, automatic processing of forms written in Kannada language is considered. A suitable pre-processing technique is presented for extracting handwritten characters. Principal Component Analysis (PCA) and Histogram of oriented Gradients (HoG) are used for feature extraction. These features are fed to multilayer feed forward back pro...

1 Introduction Typically, models of neural networks are divided into two categories in terms of signal transmission manner: feed-forward neural networks and recurrent neural networks. They are built up using different frameworks, which give rise to different fields of applications. Feed-forward neural network (FNN), also referred to as multilayer percep-trons (MLPs), has drawn great interests o...