Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Stability analysis of an adaptive fuzzy control system using Petri Nets and learning automata
Mathematics and Computers in Simulation - Special issue from the IMACS/IFAC international symposium on soft computing methods and applications: “SOFTCOM '99” (held in Athens, Greece)
Information Sciences—Informatics and Computer Science: An International Journal
Simulations of quantum neural networks
Information Sciences—Informatics and Computer Science: An International Journal - Special Issue on Quantum Computing and Neural Information Processing
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Quantum computation and quantum information
Quantum computation and quantum information
A Simple Robust Sliding-Mode Fuzzy-Logic Controller of the Diagonal Type
Journal of Intelligent and Robotic Systems
Morphogenic neural networks encode abstract rules by data
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Intelligent information systems and applications
Fuzzy Stochastic Automata for Reactive Learning and Hybrid Control
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Neural Structures Using the Eigenstates of a Quantum Harmonic Oscillator
Open Systems & Information Dynamics
Attractor models of working memory and their modulation by reward
Biological Cybernetics
Neurodynamics and attractors in quantum associative memories
Integrated Computer-Aided Engineering
Stability analysis of social foraging swarms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Parallelization of a fuzzy control algorithm using quantum computation
IEEE Transactions on Fuzzy Systems
Bipolar spectral associative memories
IEEE Transactions on Neural Networks
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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.