in this paper, the artificial neural network (ann) approach is applied for forecasting groundwater level fluctuation in aghili plain,southwest iran. an optimal design is completed for the two hidden layers with four different algorithms: gradient descent withmomentum (gdm), levenberg marquardt (lm), resilient back propagation (rp), and scaled conjugate gradient (scg). rain,evaporation, relative...

in this paper, the artificial neural network (ann) approach is applied for forecasting groundwater level fluctuation in aghili plain,southwest iran. an optimal design is completed for the two hidden layers with four different algorithms: gradient descent withmomentum (gdm), levenberg marquardt (lm), resilient back propagation (rp), and scaled conjugate gradient (scg). rain,evaporation, relative...

This paper examines how real-time information gathered as part of intelligent transportation systems can be used to predict link travel times for one through five time periods ahead (of 5-min duration). The study employed a spectral basis artificial neural network (SNN) that utilizes a sinusoidal transformation technique to increase the linear separability of the input features. Link travel tim...

With the rapid evolution of telecommunication networks, real-time traffic management is becoming more and more crucial. We propose here a neural network modular architecture for performing diagnosis at different levels of the telephone network. This system is designed as an aid to the operator. We present results of different experiments with a first version of the architecture which mainly ope...

Abs t r ac t . We present a neural network approach to human face detection. Using a modular system, a conditional mixture of networks, we a r e able to detect front view faces as well as turned faces (up to 50 degrees) with excellent performances. This modular network is integrated into LISTEN, our face tracking system. It enables this system to detect and track in real-time faces in a variety...

We apply the partition algorithm to the problem of time-series classification. We assume that the source that generates the time series belongs to a finite set of candidate sources. Classification is based on the computation of posterior probabilities. Prediction error is used to adaptively update the posterior probability of each source. The algorithm is implemented by a hierarchical, modular,...

This paper demonstrates the advantages of using a hybrid reinforcement–modular neural network architecture for non-linear control. Specifically, the method of ACTION-CRITIC reinforcement learning, modular neural networks, competitive learning and stochastic updating are combined. This provides an architecture able to both support temporal difference learning and probabilistic partitioning of th...

To investigate the relations between structure and function in both artificial and natural neural networks, we present a series of simulations and analyses with modular neural networks. We suggest a number of design principles in the form of explicit ways in which neural modules can cooperate in recognition tasks. These results may supplement recent accounts of the relation between structure an...

We argue that hierarchical methods can become the key for modular robots achieving reconfigurability. We present a hierarchical approach for modular robots that allows a robot to simultaneously learn multiple tasks. Our evaluation results present an environment composed of two different modular robot configurations, namely 3 degrees-of-freedom (DoF) and 4DoF with two corresponding targets. Duri...