Abstract
Agricultural Engineers and Biologists often need to develop and use functions in order to explain observed events, perform predictions under different scenarios, find relevant characteristics of the data, such as growth rates, etc.. The purpose of curve fitting is to specify an appropriate function and adjust its parameters in a way that it matches, as close as possible, an experimental or historical data set. Fitting functions can be derived by applying regression techniques, interpolation, and spline curves. This paper deals with the development and application of linear and nonlinear regression techniques for fitting functions to agricultural and biological data sets. The application and utility of these fitting techniques are illustrated using experimental sets of data. The examples cover from simple linear curve fitting techniques, which use least squares algorithms, to complex real world problems, which must be described by functions that are nonlinear in the parameters and so needs the use of nonlinear regression techniques. In these illustrative examples, a particular attention is paid to the validation and adequacy of the fitting functions, using graphical inspection and numerical performance criteria's, such as: statistical analysis of the residuals, the R-square, adjusted R-square, the RMSE-Root mean squared errors, confidence bounds for the estimated function parameters and prediction bounds for the fitted functions.

Abstract
A genetic algorithm (GA) is a search technique based on the natural selection process. The GAs provide further flexibility and robustness that are unique for signal process. Recently, a closer look of some phenomena present in electrical systems, such as motors, transformers and lines, and the motivation towards the development of comprehensive models, seem to point out the requirement for a fractional calculus approach. Bearing these ideas in mind, we address the analysis and the synthesis of fractional-order multipoles, based in a GA optimization scheme. © 2006 Civil-Comp Press.

Abstract
The Maxwell equations constitute a formalism for the development of models describing electromagnetic phenomena. The four Maxwell laws have been adopted successfully in many applications and involve only the integer order differential calculus. Recently, a closer look for the cases of transmission lines, electrical motors and transformers, that reveal the socalled skin effect, motivated a new perspective towards the replacement of classical models by fractional-order mathematical descriptions. Bearing these facts in mind this paper addresses the concept of static fractional electric potential. The fractional potential was suggested some years ago. However, the idea was not fully explored and practical methods of implementation were not proposed. In this line of thought, this paper develops a new approximation algorithm for establishing the fractional order electrical potential and analyzes its characteristics.

Abstract
Particle Swarm Optimization (PSO) is a population-based search algorithm that is initialized with a population of random solutions, called particles. In a PSO scheme each particle flies through the search space with a velocity that is adjusted dynamically according with its historical behavior. Therefore, the particles have a tendency to fly towards the best search area along the search process. PSO is also an evolutionary computation technique well adapted to the automatic design of electronic devices. In this line of thought, this paper proposes a PSO based algorithm for logic circuit synthesis. The results show the statistical characteristics of this algorithm with respect to number of generations required to achieve the solutions. The results are compared with other two Evolutionary Algorithms (EAs), namely Genetic and Memetic Algorithms (GA and MA).

Abstract
The concept of differentiation and integration to non-integer order has its origins in the nineteen century. However, only in the second-half of the twenty century appeared the first applications related to the area of control theory. In this paper we consider the study of a heat diffusion system based on the application of the fractional calculus concepts. In this perspective, several control methodologies are investigated and compared, such as the adoption of a Smith predictor structure for the better control of systems with time delays. Several simulations are presented assessing the performance of the proposed fractional-order algorithms.

Abstract
This paper analyzes the performance of two cooperative robot manipulators. It is studied the implementation of fractional-order algorithms in the position/force control of two robots holding an object. The experiments reveal that fractional algorithms lead to performances superior to classical integer-order controllers.