A genetic algorithm is used for the topology design of feed-forward neural networks. The generated neural network topology populations are then trained to approximate non-linear relationships of multiple variables. A specifically designed fitness function using the epistemological principle of dimensional homogeneity is used for the evaluation of the individual neural network generations and fo...

In this paper, the nonlinear autoregressive model with exogenous variables as a new neural network is used for timing of the stock markets on the basis of the technical analysis of Japanese Candlestick. In this model, the “nonlinear autoregressive model with exogenous variables” is an analyzer. For a more reliable comparison, here (like the literature) two approaches of  Raw-based and Signal-ba...

In this paper, model reference neural network structure is used as a controller for vibration suppression of the Euler–Bernoulli beam under the excitation of moving mass travelling along a vibrating path. The non-dimensional equation of motion the beam acted upon by a moving mass is achieved.  A Dirac-delta function is used to describe the position of the moving mass along the beam and its iner...

In current scenario of software industries, Software effort estimation is very important task for software manager for successful completion of the project. Prediction is always challenging task and in recent days effort estimation take many researcher’s attention. Prediction with more accuracy is also an important for prediction models. We use Feed-Forward Neural Network for software developme...

Abstract. Various machine learning methods were attempted in the global mapping of surface ocean partial pressure CO2 (pCO2) to reduce uncertainty sink estimate due undersampling pCO2. In previous research, predictors pCO2 usually selected empirically based on theoretic drivers pCO2, and same combination was applied all areas except where there a lack coverage. However, differences between diff...

Forecasting a hydrologic time series has been one of the most complicated tasks owing to the wide range of data, the uncertainties in the parameters influencing the time series and also due to the non availability of adequate data. Recently Artificial Neural Networks (ANN) have become quite popular in time series forecasting in various fields. This paper demonstrates the use of ANN to forecast ...

In this paper, we investigated interactive learning between human subjects and robot experimentally, and its essential characteristics are examined using the dynamical systems approach. Our research concentrated on the navigation system of a specially developed humanoid robot called Robovie and seven human subjects whose eyes were covered , making them dependent on the robot for directions. We ...

This paper, presents a theoretical and practical basis of preprocessing on handwritten text for character recognition using forward-feed neural networks. Afterwards, the Feed forward algorithm gives working of a neural network followed by the Back Propagation Algorithm which compromises Training, Calculating Error, and Modifying Weights. The proposed solutions focus on applying Back Propagation...

In this paper we address the problem of rejecting Out-Of-Vocabulary words in speaker-independent Mandarin place name recognition. We integrate neural network and Hidden Markov Models in an attempt to utilize the strength of both. HMM based acoustic models including keyword models, filler models, and an anti-keyword model were trained to meet our needs. Statistical features are fed to a neural n...

In this paper, a circuit system of General Purposed Conic Section Function Neural Network is presented. The feed-forward analog computational cells have been designed by using the current mode approach. The network is trained in a chip-in-the-loop fashion with a host computer implementing the training algorithm. The network inputs and the feed-forward signal processing are analog. The mixed ana...