Neural Structures Using the Eigenstates of a Quantum Harmonic Oscillator

The main result of the paper is the use of orthogonal Hermite polynomials as the basis functions of feedforward neural networks. The proposed neural networks have some interesting properties: (i) the basis functions are invariant under the Fourier transform, subject only to a change of scale, (ii) the basis functions are the eigenstates of the quantum harmonic oscillator, and stem from the solution of Schrödinger's diffusion equation. The proposed feed-forward neural networks demonstrate the particle-wave nature of information and can be used in nonparametric estimation. Possible applications of the proposed neural networks include function approximation, image processing and system modelling.     

Extended Kalman and Particle Filtering for sensor fusion in motion control of mobile robots

Motion control of mobile robots and efficient trajectory tracking is usually based on prior estimation of the robots’ state vector. To this end Gaussian and nonparametric filters (state estimators from position measurements) have been developed. In this paper the Extended Kalman Filter which assumes Gaussian measurement noise is compared to the Particle Filter which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of a mobile robot is used, when measurements are available from both odometric and sonar sensors. It is shown that in this kind of sensor fusion problem the Particle Filter has better performance than the Extended Kalman Filter, at the cost of more demanding computations.     

Stochastic Processes and Neuronal Modelling: Quantum Harmonic Oscillator Dynamics in Neural Structures

This paper studies neural structures with weights that follow the model of the quantum harmonic oscillator (Q.H.O.). The proposed neural networks have stochastic weights which are calculated from the solution of Schrödinger’s equation under the assumption of a parabolic (harmonic) potential. These weights correspond to diffusing particles, which interact to each other as the theory of Brownian motion (Wiener process) predicts. The learning of the stochastic weights (convergence of the diffusing particles to an equilibrium) is analyzed. In the case of associative memories the proposed neural model results in an exponential increase of patterns storage capacity (number of attractors). Finally, it is shown that conventional neural networks and learning algorithms based on error gradient can be conceived as a subset of the proposed quantum neural structures. Thus, the complementarity between classical and quantum physics is also validated in the field of neural computation.     

Parallelization of a fuzzy control algorithm using quantum computation

Quantum computation is proposed for the parallelization of a fuzzy logic control (FLC) algorithm. Quantum computation speeds up the fuzzy inference since serial operations between matrices of large dimensionality are now replaced by a one-step quantum addition or a quantum subtraction. The unitarity properties of the algorithm prove that the FLC stands for a simulator of a quantum computing machine.     

Stability conditions and numerical solutions on oscillatory thermoviscous shearing

We study the analytical and numerical behaviour of the adiabatic shearing flow of an incompressible Newtonian liquid with temperature-dependent viscosity, under a time-periodic boundary velocity. We give sufficient stability conditions for the solution of the governing balance and constitutive equations and we present numerical results for the asymptotic convergence of the flow. Essentially, we verify that the stress decays to a time oscillatory function while the temperature exhibits a strongly non-uniform distribution with its maximum value tending to infinity with time.     

Fuzzy stochastic automata for intelligent vehicle control

Fuzzy stochastic automata (FSA) are proposed for the control of autonomous vehicles. FSA merge the concept of sliding-mode control with fuzzy logic and have interesting robustness properties. Sufficient conditions for the convergence of the FSA control are provided.     

Particle Filtering for State Estimation in Nonlinear Industrial Systems

State estimation is a major problem in industrial systems, particularly in industrial robotics. To this end, Gaussian and nonparametric filters have been developed. In this paper, the extended Kalman filter, which assumes Gaussian measurement noise, is compared with the particle filter, which does not make any assumption on the measurement noise distribution. As a case study, the estimation of the state vector of an industrial robot is used when measurements are available from an accelerometer that was mounted on the end effector of the robotic manipulator and from the encoders of the joints' motors. It is shown that, in this kind of sensor fusion problem, the particle filter outperforms the extended Kalman filter, at the cost of more demanding computations.     

Geometry and Thermal Regulation of GMA Welding via Conventional and Neural Adaptive Control

This paper investigates the application of conventional and neural adaptive control schemes to Gas Metal Arc (GMA) welding. The goal is to produce welds of high quality and strength. This can be achieved through proper on-line control of the geometrical and thermal characteristics of the process. The welding process is variant in time and strongly nonlinear, and is subject to many defects due to improper regulation of parameters like arc voltage and current, or travel speed of the torch. Adaptive control is thus naturally a very good candidate for the regulation of the geometrical and thermal characteristics of the welding process. Here four adaptive control techniques are reviewed and tested, namely: model reference adaptive control (MRAC), pseudogradient adaptive control (PAC), multivariable self-tuning adaptive control (STC), and neural adaptive control (NAC). Extensive numerical results are provided, together with a discussion of the relative merits and limitations of the above techniques.     

Fuzzy reinforcement learning control for compliance tasks ofrobotic manipulators

A fuzzy reinforcement learning (FRL) scheme which is based on the principles of sliding-mode control and fuzzy logic is proposed. The FRL uses only immediate reward. Sufficient conditions for the convergence of the FRL to the optimal task performance are studied. The validity of the method is tested through simulation examples of a robot which deburrs a metal surface.     

On the “inertial” time in instability of materials with non-monotone yield stress

Summary We consider the shear instability problem of a ductile material which exhibits strain softening response after a critical value of the plastic strain, as well as strain-rate sensitivity. We first show that the time which corresponds to the maximum stress cannot be considered as the exact critical time at which instability occurs, by establishing the existence of an inertial time, which extends the stability regime into the softening region. We study the parameters that influence the value of this inertial time and show the importance of the strain-rate sensitivity. More precisely, we show that its presence, independent of its value, is sufficient to ensure the prolongation of the stability regime after the maximum value of the yield function. Finally, we present the numerical simulation of the non-linear problem to show that there exists a transition regime after the maximum stress, in which the strain-rate non-uniformities evolve very slowly. This regime is followed by an unstable situation, in the sense that the strain-rate localizes in the softer regions. The localization is due to a feed-back mechanism, which favors the strain increase much more at the softer regions, forcing the stress to decrease much more there, which, in turn, allows for the development of larger strain-rate values.