Chaos-Geometric Attractor and Quantum Neural Networks Approach to Simulation Chaotic Evolutionary Dynamics During Perception Process

摘要

Nonlinear simulation and forecasting chaotic evolutionary dynamics during perception and tuition processes can be effectively performed using the concept of compact geometric attractors. We present a new approach to analyze and predict the nonlinear perception and tuition dynamics based on the concept of geometric attractors, chaos theory methods and algorithms for quantum neural network simulation. Using phase space information on the evolution of the perception and tuition processes in time and results of the of quantum neural network modelling techniques can be considered as one of the fundamentally new approaches in the construction of global nonlinear models of the most effective and accurate description of the structure of the corresponding attractor and in further optimal realizations of the perception and tuition processes.