Abstract

Abstract
Fractional calculus (FC) is no longer considered solely from a mathematical viewpoint, and is now applied in many emerging scientific areas, such as electricity, magnetism, mechanics, fluid dynamics, and medicine. In the field of dynamical systems, significant work has been carried out proving the importance of fractional order mathematical models. This article studies the electrical impedance of vegetables and fruits from a FC perspective. From this line of thought, several experiments are developed for measuring the impedance of botanical elements. The results are analyzed using Bode and polar diagrams, which lead to electrical circuit models revealing fractional-order behaviour.

Abstract
This paper proposes and analyses the performance of a Genetic Algorithm (GA) using two new concepts, namely a static fitness function including a discontinuity measure and a fractional-order dynamic fitness function. The GA is adopted for the synthesis of combinational logic circuits. In both cases, experiments reveal superior results in terms of speed and convergence to achieve a solution.

Abstract
This paper describes a computer-based temperature control system for an environmental chamber. The identification of the thermal process was made from the analysis of the collected output data in response to random generated input signals. Using this model it was established the optimal linear and non linear control strategies in order to achieve the desired set-point and to minimize the number of actuations. The actuation over the heating and cooling systems were performed with conventional PID and fuzzy controllers and their responses were compared. This work enhances fuzzy controllers application with self-learning capability to achieve the prescribed control objectives in a near-optimal manner. By applying back-propagation through time it was possible to force the plant block to generate the desired trajectory.

Abstract
This paper presents two evolutionary schemes and a swarm intelligence algorithm for the design of combinational logic circuits. A Genetic and a Memetic schemes as the evolutionary algorithms. The Particle Swarm Optimization as the swarm algorithm. The fitness function used in these three algorithms is sequential, that is, divided in two parts. The first part of the fitness function f(1) evaluates the circuit functionality, while the second part f(2) deals with the circuit complexity. The experiments consist in applying the algorithms in the design of two arithmetic circuits: the one-bit full adder and the one-bit full subtractor. We also present a scalability analysis using the parity checker family of circuits.

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).