Memristor-Based CMAC Neural Network Circuit of Artificial Fish Behavioral Decision With Fuzzy Emotion and Its Application
Junwei Sun, K. Tao, Shiping Wen, Zicheng Wang, Yanfeng Wang
Abstract
Current biological behavior models only take the external environment information as the basis for decision-making, ignoring the internal emotional state information. A memristor-based cerebellar model articulation controller (CMAC) neural network circuit of artificial fish behavioral decision is designed, and fuzzy emotion is taken into account. The designed circuit is mainly composed of voltage selection modules, fuzzy processing modules, synaptic neuron modules, eigen quantity modules and feedback modules. CMAC neural network is used as learning criteria and the learning subspace voltage with emotional generalization properties outputs to synaptic neural module. By utilizing the nonvolatility and thresholding properties of the memristor, the weights in the neural network are changed to enable the artificial fish to perform primary and secondary learning under specific emotional voltages. The feasibility of the above circuit is verified by PSpice simulation software. The artificial life and biological intelligence behavior are integrated by the memristor-based CMAC neural network circuit. It provides a reliable theory and basis for the emotional behavior of bionic robots.