Introduction What is an artificial neural network and how does it work? Artificial neural network have been developed from generalizations of neural biology model, based on the assumptions that (a) information processing occurs at many simple elements called neurons, (b) signals are passed between neurons over connection links, (c) each connection link has an associated weight, which, in a typi...

In order to track a maneuvering target, multiple model (MM) methods have been researched. Almost MM algorithms have been developed based on Markov process. However, Markov based MM method is difficult to design and application-dependent. To solve this problem, Daebum Choi, et al proposed basic idea of neural-net based VSMM [5]. In this paper, we will show the design procedure of neural-net base...

Although recurrent neural nets have been moderately successful in learning to emulate nite-state machines (FSMs), the continuous internal state dynamics of a neural net are not well matched to the discrete behavior of an FSM. We describe an architecture, called DOLCE, that allows discrete states to evolve in a net as learning progresses. dolce consists of a standard recurrent neural net trained...

In this paper a hybrid system and a hierarchical neural-net approaches are proposed to solve the automatic labeling problem for unsupervised clustering. The first method consists in the application of nonneural clustering algorithms directly to the output of a neural net; the second one is based on a multilayer organization of neural units. Both methods are a substantial improvement with respec...

Although recurrent neural nets have been moderately successful in learning to emulate finite-state machines (FSMs), the continuous internal state dynamics of a neural net are not well matched to the discrete behavior of an FSM. We describe an architecture, called DOLCE, that allows discrete states to evolve in a net as learning progresses. DOLCE consists of a standard recurrent neural net train...

Neural networks show a progressive increase in complexity during the time course of evolution. From diffuse nerve nets in Cnidaria to modular, hierarchical systems in macaque and humans, there is a gradual shift from simple processes involving a limited amount of tasks and modalities to complex functional and behavioral processing integrating different kinds of information from highly specializ...

the main purpose of this paper is prediction of tse corporate financial bankruptcy using artificial neural networks. the mean values of key ratios reported in past bankruptcy studies were selected for neural network inputs (working capital to total assets, net income to total assets, total debt to total assets, current assets to current liabilities, quick assets to current liabilities). the neu...

Neural nets offer an approach to computation thatmimics biological nervous systems. Algorithms based on neural nets have been proposed to address speech recognition tasks which humans perlorm with little apparent effort. In this paper, neural net classifiers are described and compared with conventional classification algorithms. Perceptron classifiers trained with a new algorithm, called back p...