This paper employs the continuous-time analogue Hopfield neural network to compute the temperature distribution in forward heat conduction problems and solves inverse heat conduction problems by using a back propagation neural (BPN) network to identify the unknown boundary conditions. The weak generalization capacity of BPN networks is improved by employing the Bayesian regularization algorithm...

This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR) images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM) based texture features. Then, the features were reduced by principle component analysis (PCA). Finally, a two-hidden-layer forward neural network (NN) was constructed and trained by ...

-There have been a number of methods presented by various researchers for traffic prediction, some of which involve modeling the problem of traffic prediction as a time series. It has been observed that Artificial Neural Networks (ANN) perform better than statistical methods for time series forecasting. The network performance and complexity varies with the choice of algorithm used. Back propag...

Two methods to correct phonemic transcriptions produced by the acoustic processor of a speech recognition system are described and compared. The first method that was invented by Prof. Teuvo Kohonen and named the Dynamically Expanding Context (DEC), involves a large set of error-correcting rules automatically constructed from examples. This symbolic approach is compared with a connectionist one...

This paper contributes an effective recognition approach for Myanmar Handwritten Characters. In this article, Hybrid approach use ICR and OCR recognition through MICR (Myanmar Intelligent Character Recognition) and backpropagation neural network. MICR is one kind of ICR. It composed of statistical/semantic information and final decision is made by voting system. In Hybrid approach, the features...

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...

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...