Quantum fuzzy inference for knowledge base design in robust intelligent controllers

Self-Organized Intelligent Robust Control Based on Quantum Fuzzy Inference

“…In this case between KB1 and KB2 (from quantum information theory of viewpoint) we organize a communication channel using quantum correlations that is impossible in classical communication theory. The algorithm of superposition calculation is presented below and described in details in (Litvintseva et al, 2007). We discuss for simplicity the situation in which an arbitrary amount of correlation is unlocked with a one-way message.…”

“…The solution of this problem based on SCO of KB was developed (Litvintseva et al, 2006). For achieving of self-organization level in intelligent control system it is necessary to use QFI (Litvintseva et al, 2007). The described self-organizing FC design method is based on special form of QFI that uses a few of partial KBs designed by SCO.…”

“…Physically, QFI supports optimally a new developed thermodynamic trade-off of control performance (between stability, controllability and robustness) in self-organization KB process. From quantum information theory viewpoint, QFI reduces the redundant information in classical control signals using four facts (Litvintseva et al, 2007) from quantum information for data compression in quantum information processing: 1) efficient quantum data compression; 2) coupling (separation) of information in the quantum state in the form of classical and quantum components; 3) amount of total, classical, and quantum correlation; and 4) hidden (observable) classical correlation in the quantum state. We are developed the gate structure of QFI model with self-organization KB properties that includes all of these QA features (see, below Fig.…”

“…1) based on QFI model as the background of robust KB design information technology. QFI model is described in details (Litvintseva et al, 2007) and used here as toolkit. Fig.…”

“…Optimal drawing process of value information from a few KBs that are designed by soft computing is based on following four facts from quantum information theory (Litvintseva et al, 2007): (i) the effective quantum data compression; (ii) the splitting of classical and quantum parts of information in quantum state; (iii) the total correlations in quantum state are "mixture" of classical and quantum correlations; and (iv) the exiting of hidden (locking) classical correlation in quantum state. This quantum control algorithm uses these four Facts from quantum information theory in following way: (i) compression of classical information by coding in computational basis { } 0,1 and forming the quantum correlation between different computational bases (Fact 1); (ii) separating and splitting total information and correlations on "classical" and "quantum" parts using Hadamard transform (Facts 2 and 3); (iii) extract unlocking information and residual redundant information by measuring the classical correlation in quantum state (Fact 4) using criteria of maximal corresponding amplitude probability.…”