modelling and forecasting stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. this nonlinearity affects the efficiency of the price characteristics. using an artificial neural network (ann) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

bedload transport is an essential component of river dynamics and estimation of its rate is important to many aspects of river management. in this study, measured bedload by helley- smith sampler was used to estimate the bedload transport of kurau river in malaysia. an artificial neural network, genetic programming and a combination of genetic programming and a neural network were used to estim...

One of the classical topics in neural networks is winner-take-all (WTA), which has been widely used in unsupervised (competitive) learning, cortical processing, and attentional control. Owing to global connectivity, WTA networks, however, do not encode spatial relations in the input, and thus cannot support sensory and perceptual processing where spatial relations are important. We propose a ne...

We propose a new self-organizing neural model that performs principal components analysis. It is also related to the adaptive subspace self-organizing map (ASSOM) network, but its training equations are simpler. Experimental results are reported, which show that the new model has better performance than the ASSOM network. KeywordsSelf-organization; Principal component analysis; Competitive lear...

In order to perform object recognition it is necessary to learn representations of the underlying components of images. Such components correspond to objects, object-parts, or features. Nonnegative matrix factorisation is a generative model that has been specifically proposed for finding such meaningful representations of image data, through the use of non-negativity constraints on the factors....

Competitive learning is an unsupervised algorithm that classifies input patterns into mutually exclusive clusters. In a neural net framework, each cluster is represented by a processing unit that competes with others in a winnertake-all pool for an input pattern. I present a simple extension to the algorithm that allows it to construct discrete, distributed representations. Discrete representat...

The proposed IAFC neural networks have both stability and plasticity because theyuse a control structure similar to that of the ART-1(Adaptive Resonance Theory) neural network.The unsupervised IAFC neural network is the unsupervised neural network which uses the fuzzyleaky learning rule. This fuzzy leaky learning rule controls the updating amounts by fuzzymembership values. The supervised IAFC ...

The paper aims to build up a common color codebook, which represents well the database color information under consideration to improve the performance of the image retrieval problem. The frequency sensitive competitive learning neural network algorithm is used for this task, in order to form the set of prototypes that represents well the environment. The similarity measure is a performed aid o...

The use of ensemble models in many problem domains has increased significantly in the last few years. The ensemble modeling, in particularly boosting, has shown a great promise in improving predictive performance of a model. Combining the ensemble members is normally done in a co–operative fashion where each of the ensemble members performs the same task and their predictions are aggregated to ...

This work presents a way to cluster HTML document sets in an hierarchical manner. The hierarchical clustering is performed using the Hierarchical Radius-based Competitive Learning (HRCL) neural network that has been developed by the authors. After a detailed discussion of the algorithm, HRCL clustering as well as retrieval results will be presented. The HRCL clustering results in a hierarchical...