The text area is 29 × 45 picas (~12.3 cm × 19 cm) to fit in the printed proceedings book without reduction. When printed on letter paper there will be wide margins. Abstract. Previous use of Artificial Intelligence (AI) in agriculture for forecasting productivity indicators, especially Artificial Neural Networks (ANN), has shown that it is possible to approximate sugarcane maturation curves. Ho...

Artificial neural networks (ANNs) are a form of artificial intelligence and, since the mid-1990s, ANNbased models have been successfully applied to virtually every problem in geotechnical engineering. This paper briefly examines the areas of geotechnical engineering to which ANNs have been applied, provides a brief overview of the operation of ANN models, and highlights and discusses four impor...

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 ...

The possibility of developing a machine that would “think” has intrigued human beings since ancient times. Artificial intelligence (AI) systems comprise two major areas, expert systems (ES) and artificial neural networks (ANNs). The major objective of this paper is to illustrate how artificial intelligence techniques might play an important role in modelling and prediction of the performance of...

An important reason for the continued popularity of Artificial Neural Networks (ANNs) in the machine learning community is that the gradient-descent backpropagation procedure gives ANNs a locally optimal change procedure and, in addition, a framework for understanding the ANN learning performance. Genetic programming (GP) is also a successful evolutionary learning technique that provides powerf...

Artificial Neural Networks (ANNs) have demonstrated very interesting properties in adaptive identification schemes and control laws. In this work, they are employed for the on-line control strategy of an Active Power Filter (APF) in order to improve its performance. Indeed, neural-based approaches are synthesized to design adaptive and efficient harmonic identification schemes. The proposed neu...

For Artificial Neural Networks (ANNs) to be effective modelling tools, they must draw upon biological characteristics: One characteristic often overlooked in the design of ANNs is the replication, or redundancy, of processes within the brain. This paper examines the effects of redundancy on the performance of ANNs trained on either a pattern classification task (e.g. parity, encoder) or a funct...

This paper investigates the use of a multi-objective approach for evolving artificial neural networks that act as controllers for the legged locomotion of a quadrupedal robot simulated in a 3-dimensional, physics-based environment. The Pareto-frontier Differential Evolution (PDE) algorithm is used to generate a Pareto optimal set of artificial neural networks that optimize the conflicting objec...