In recent years, there has been more attention to predict the behavior of vessel in the sea (sea keeping). The more the deeps of vessel increases in the high speed and light vessels, the more calculations are necessary. In this paper, a BP (back propagation) neural network is presented that keeps sea keeping indexes under the categories of input and output of the network. Evaluation is based on...

background and objectives: rheological characteristics of dough are important for achieving useful information about raw-material quality, dough behavior during mechanical handling, and textural characteristics of products. our purpose in the present research is to apply soft computation tools for predicting the rheological properties of dough out of simple measurable factors. materials and met...

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...

We discuss the simulation of reaction times in connectionist systems. The obvious way to do this involves thinking in terms of neural activations building up towards some threshold in cascaded systems, but it has also been suggested that the output activation error scores in standard back-propagation networks should also be correlated with response times. The idea is that in the more realistic ...

It has been shown that a trained back-propagation neural network (BPNN) classi er with Kullback-Leibler criterion produces outputs which can be interpreted as estimates of Bayesian a posteriori probabilities. Based on this interpretation, we propose a back-propagation neural network (BPNN) approach for the estimation of the local conditional distributions of textured images, which are commonly ...

An improved complex-valued back propagation neural network (ICVBPNN) algorithm is proposed in this paper. In allusion to the defect of gradient descent of traditional complex-valued back propagation network (CVBPNN) algorithm, additive momentum has been introduced. It is used for time-varying channel tracking and prediction in wireless communication system and better application results are acq...

Rumelhart (1987). has proposed a method for choosing minimal or "simple" representations during learning in Back-propagation networks. This approach can be used to (a) dynamically select the number of hidden units. (b) construct a representation that is appropriate for the problem and (c) thus improve the generalization ability of Back-propagation networks. The method Rumelhart suggests involve...

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...