초록 ▼

A system for obtaining optimum performance and optimum graceful degradation from Lie algebra descriptions of a spectrum of reconfigurable network architectures, including, neural nets and cellular automata comprised of interconnected nodes. The dynamic performance of the computational process is mon

A system for obtaining optimum performance and optimum graceful degradation from Lie algebra descriptions of a spectrum of reconfigurable network architectures, including, neural nets and cellular automata comprised of interconnected nodes. The dynamic performance of the computational process is monitored by continued extraction of Liapounov exponent indicators, reconfiguring said reconfigurable network architecture when said indicators predict non-optimum performance. The reconfigurable networks are reconfigured and compensatory adjustments are made of signal sampling performance and operating system performance of said reconfigurable network architecture, and the operating system architecture is optimized to the computational task by reconfiguration of nodal capabilities and degree of interconnectedness between nodes to obtain any Lie algebra description architectural form between ideal neural net with maximum interconnectedness and ideal cellular automata with maximum nodal capability.

대표청구항 ▼

A method for obtaining optimum performance and optimum degradation from Lie algebra descriptions of a spectrum of reconfigurable network architectures, including, neural nets and cellular automata comprised of interconnected nodes having dynamic performance characteristics of a computational process

A method for obtaining optimum performance and optimum degradation from Lie algebra descriptions of a spectrum of reconfigurable network architectures, including, neural nets and cellular automata comprised of interconnected nodes having dynamic performance characteristics of a computational process, comprising: monitoring the dynamic performance characteristic of the computational process by continued extraction of Liapounov exponent indicators, reconfiguring said reconfigurable network architecture when said Liapounov exponent indicators predict non-optimum performance, making compensatory adjustments of signal sampling performance and operating system performance of said reconfigurable network architecture, and optimizing said reconfigurable network architecture to the computational process by reconfiguration of nodal capabilities and degree of interconnectedness between nodes to obtain a Lie algebra description architectural form between ideal neural net with maximum interconnectedness and ideal cellular automata with maximum nodal capability.