Alberto Delgado

A novel multiobjective optimization algorithm based on bacterial chemotaxis

In this article a novel algorithm based on the chemotaxis process of Echerichia coli is developed to solve multiobjective optimization problems. The algorithm uses fast nondominated sorting procedure, communication between the colony members and a simple chemotactical strategy to change the bacterial positions in order to explore the search space to find several optimal solutions. The proposed algorithm is validated using 11 benchmark problems and implementing three different performance measures to compare its performance with the NSGA-II genetic algorithm and with the particle swarm-based algorithm NSPSO.

Phase portrait approximation using dynamic neural networks

In this paper it is shown that a nonlinear system with an isolated equilibrium can be identified using two kinds of Dynamic Neural Networks, the Hopfield type (DNN-H) and the Multilayer type (DNN-M). The identified models are in continuous time. The equilibrium point is assumed to be the origin, without loss of generality, and it is assumed to be stable. Two different approaches are used to identify the nonlinear system, the first one is a phase portrait identification and the second one is the identification of the input/output response. The phase portraits for the two models are plotted and the eigenvalues of the Jacobian of the plant and the Jacobian of the models are calculated to make comparisons. Both networks can be used to identify nonlinear systems. The DNN Multilayer type is used to keep the number of states at a minimum, i.e., the number of states of the network is the same as the plant, the approximation capabilities of the DNN-M are increased by increasing the number of neurons of the internal multilayer network keeping the number of states constant. The DNN Hopfield type is used to obtain a state representation without hidden units but the number of states is not minimum, that is, the DNN-H equations are simpler but the number of states may be higher than the number of states of the plant.

Noise generation in dynamic wireless power transfer

In this paper a basic mathematical model is introduced to study noise generation in a resonant circuit moving with constant velocity along a road with evenly distributed coils each one excited at the resonance frequency. The induced current can be contaminated by noise generated when velocity of the vehicle, coils period, or spread of the coupling coefficient are changed; the model is illustrated with simulations.

Magnetically coupled circuits: Moving coils

The main motivation for this paper is to explore, using circuit theory and simulations, magnetically coupled circuits with moving coils; in the first example there are two coils, one that creates a static magnetic field and the second following an elliptical path causing a time dependent coupling coefficient. The second case involves three coils, one moving, two static and each one generating a constant magnetic field.

Magnetically coupled circuits as dynamic systems

Magnetically coupled circuits can be formulated in the language of dynamic systems for both time variant and time invariant inductances. An ideal system, proposed here, with zero resistance and zero self-inductance for each circuit can be used to guide the design of a computing device that finds the minima of an energy function.

Gene network motifs as dynamic systems

Gene networks are a particular class of complex networks found in living organisms. In these networks, there are some statistically significant connection patterns called motifs. The goal of this paper is to study mainly four motifs FFL, SIM, MO-FFL, BIFAN, present in the E. Coli gene network from the control theory perspective.

Rule based system with DNA chip

DNA chips are proposed as the platform to store and evaluate rules for knowledge based systems. The principle is illustrated with two rule bases, Boolean and fuzzy.

Fault detection and classification with DNA chips

DNA chips are currently used for gene expression studies of cells and tissues under different conditions. The same principle can be applied in control engineering to supervise industrial plants, determine operating conditions, detect or classify faults using thousands of sampled analog or binary variables.