-Fuzzy ARTMAP is one of the recently proposed neural network paradigm where the fuzzy logic is incorporated. In this paper, we compare the Fuzzy ARTMAP neural network and the well-known back-propagation based Multi-layer perceptron (MLP), in the context of hand-written character recognition problem. The results presented in this paper shows that the Fuzzy ARTMAP out-performs its counterpart, bo...

A globally convergent homotopy method is defined that is capable of sequentially producing large numbers of stationary points of the multi-layer perceptron mean-squared error surface. Using this algorithm large subsets of the stationary points of two test problems are found. It is shown empirically that the MLP neural network appears to have an extreme ratio of saddle points compared to local m...

This work presents results of the use of a wavelet filter for noise reduction and data compression of signals generated by artificial nose sensors. To verify the performance of the wavelet analysis in the treatment of odor patterns, we compare two widely used artificial nose classifiers, multi-layer perceptron neural network and time delay neural network in the analysis of signals generated by ...

Classification of heart sound signals to normal or their classes of disease are very important in screening and diagnosis system since various applications and devices that fulfilling this purpose are rapidly design and developed these days. This paper states and alternative method in improving classification accuracy of heart sound signals. Standard and improvised Multi-Layer Perceptron (MLP) ...

This paper proposes a new method for prediction of chaotic time series based on Parallel Multi-Layer Perceptron (PMLP) net and dynamics reconstruction technique. The PMLP contains a number of multi-layer perceptron (MLP) subnets connected in parallel. Each MLP subnet predicts the future data independently with a different embedding dimension. The PMLP determines the final predicted result accor...

Air pollution emerges as an imminent issue in metropolitan cities like Hong Kong, and attracts much attention in recent years. Prediction of pollutant levels and their tendency is an important topic in environmental science today. To achieve such prediction tasks, the use of neural network (NN), in particular, the multi-layer perceptron, is regarded as a cost-e7ective technique superior to trad...

<span id="docs-internal-guid-54b35aa6-7fff-0992-ed4c-aca4d05cfcfa"><span>Underwater image enhancement (UIE) is an imperative computer vision activity with many applications and different strategies proposed in recent years. Underwater images are firmly low quality by a mixture of noise, wavelength dependency, light attenuation. This paper depicts effective strategy to improve the de...

Meta-learning consists in learning learning algorithms. We use a Long Short Term Memory (LSTM) based network to learn to compute on-line updates of the parameters of another neural network. These parameters are stored in the cell state of the LSTM. Our framework allows to compare learned algorithms to hand-made algorithms within the traditional train and test methodology. In an experiment, we l...

Ki-Chul Kim Dept. of Info and Comm KAIST Seoul, 130-012, Korea This paper describes a way of neural hardware implementation with the analog-digital mixed mode neural chip. The full custom neural VLSI of Universally Reconstructible Artificial Neural network (URAN) is used to implement Korean speech recognition system. A multi-layer perceptron with linear neurons is trained successfully under the...