abstract evapotranspiration as one of the important elements in agriculture has a considerable role in water resource management. therefore, using a more exact estimation method is an essential step of agricultural development, especially in arid semi-arid area. in this research, in order to exact estimate of garlic evapotranspiration using lysimeteric data, an artificial neural network (ann) m...

evaporation is one of the most important components of hydrologic cycle.accurate estimation of this parameter is used for studies such as water balance,irrigation system design, and water resource management. in order to estimate theevaporation, direct measurement methods or physical and empirical models can beused. using direct methods require installing meteorological stations andinstruments ...

this paper presents the application of three main artificial neural networks (anns) in damage detection of steel bridges. this method has the ability to indicate damage in structural elements due to a localized change of stiffness called damage zone. the changes in structural response is used to identify the states of structural damage. to circumvent the difficulty arising from the non-linear n...

A efficient incremental learning algorithm for classification tasks, called NetLines, well adapted for both binary and real-valued input patterns is presented. It generates small compact feedforward neural networks with one hidden layer of binary units and binary output units. A convergence theorem ensures that solutions with a finite number of hidden units exist for both binary and real-valued...

In this paper, we present a new fast specific complex-valued neural network, the fast Kolmogorov’s Spline Complex Network (FKSCN), which might be advantageous especially in various tasks of pattern recognition. The proposed FKSCN uses cross correlation in the frequency domain between the input data and the input weights of neural networks. It is proved mathematically and practically that the nu...

The author proposes an extension of genetic algorithm (GA) for solving fuzzy-valued optimization problems. In the proposed GA, values in the genotypes are not real numbers but fuzzy numbers. Evolutionary processes in GA are extended so that GA can handle genotype instances with fuzzy numbers. The proposed method is applied to evolving neural networks with fuzzy weights and biases. Experimental ...

Abstract: In this paper, we consider the problem of dissipativity and passivity analysis for complex-valued discrete-time neural networks with time-varying delays. The neural network under consideration is subject to time-varying. Based on an appropriate Lyapunov–Krasovskii functional and by using the latest free-weighting matrix method, a sufficient condition is established to ensure that the ...

The Hebbian paradigm is perhaps the best-known unsupervised learning theory in connectionism. It has inspired wide research activity in the artificial neural network field because it embodies some interesting properties such as locality and the capability of being applicable to the basic weight-and-sum structure of neuron models. The plain Hebbian principle, however, also presents some inherent...

mobile robot navigation is one of the basic problems in robotics. in this paper, a new approachis proposed for autonomous mobile robot navigation in an unknown environment. the proposedapproach is based on learning virtual parallel paths that propel the mobile robot toward the trackusing a multi-layer, feed-forward neural network. for training, a human operator navigates themobile robot in some...

A t ransform is introduced that maps cellular automata and discrete neural networks to dynamical systems on the unit interval. This transform is a topological conjugacy except at countably many points. In many cases, it gives rise to continuous full conjugates , in which case the transform preserves entropy. The transform also allows transfer of many dynamical properties of continuous systems t...