A Cognitive Model With Growing Struc-T Ure And Its Application To Motor Ba-Lance Control

Many skills of the biology are acquired gradually as the individual grows up, which is called as cognitive skill acquisition. It is the goal to understand and simulate the cognitive behaviors of natural life and endue these to artificial life in this thesis. A cognitive model with growing structure is presented here, and it is used in the application of motor balance skill learning and the model can control the inverted pendulum availably. The achievements in this thesis can be summarized as follows: (1) According to the achievements of neurophysiology, a cognitive model with growing structure (CM_gs) is proposed in this paper. CM_gs has reflex-like construction (RC) which is similar to the reflex arc of the biologic nerve, and RC is composed by afferent nerve, nerve center and efferent nerve. Especially, the neurons size and their topology can grow constantly. The cognitive algorithm (CA) includes work algorithm (WA) and organizing algorithm (OA). WA helps the input pattern to finding its correct response region by the competitive rules and makes the right response. OA conducts the pattern classification learning of the input stimulus and makes the network of nerve center to form the suitable size and topology. In the other hand, OA needs to make the neurons in difference fields to respond to the different stimulus with the best value through Sensitization and Habituation. (2) In CA of CM_gs, OA is the key component for the skill acquisition. OA adopts a growing algorithm (GRA) from reference to Growing Cell Structures in order to perform the pattern classification in the nerve center. This growing mechanism is able to be evolved through the continuous growing of the new neuron. Reinforcement Hebbian synaptic modification algorithm (RHA) is used as the self-learning method in CM_gs to make the neurons in difference fields to respond to the different stimulus in the best way. EA presents the constructive information for GRA and RHA. (3) Implementation scheme of CM_gs for the skill learning is proposed in this issue. CM_gs can interact autonomously with the environment and develop the motor skill by the growing manners of neural system itself through the way of "action –evaluation -reinforcement". An emulate experiment is presented to show the implement process of the skill learning by CM_gs. The results are shown that CM_gs can control a second order system whose open loop response is not stabile. And a comparison with the fuzzy control and CM_gs is maked in the paper. It is shown that Fuzzy control needs to summarize the declarative knowledge before the controlling, but CM_gs can form the spontaneous learning process to achieve the suitable network without any transcendental knowledge. (4) A cognitive model to motor balance skill (CMMBS) is provided, which is based on CM_gs, to achieve the acquisition of motor balance skill. CMMBS can...