Mohsen Firouzi

ABSTRACT Human mind from system perspective deals with high dimensional complex world as an adaptive Multi-Input Multi-Output complex system. This view is theorized by reductionism theory in philosophy of mind, where the world is represented as logical combination of simpler sub-systems for human so that operate with less energy. On the other hand, Human usually uses linguistic rules to describe and manipulate his expert knowledge about the world; the way that is well modeled by Fuzzy Logic. But how such a symbolic form of knowledge can be encoded and stored in plausible neural circuitry? Based on mentioned postulates, we have proposed an adaptive Neuro-Fuzzy machine in order to model a rule-based MIMO system as logical combination of spatially distributed Single-Input Single-Output sub-systems. Each SISO systems as sensory and processing layer of the inference system, construct a single rule and learning process is handled by a Hebbian-like Spike-Time Dependent Plasticity. To shape a concrete knowledge about the whole system, extracted features of SISO neural systems (or equivalently the rules associated with SISO systems) are combined. To exhibit the system applicability, a single link cart-pole balancer as a sensory-motor learning task, has been simulated. The system is provided by reinforcement feedback from environment and is able to learn how to get expert and achieve a successful policy to perform motor controlHuman mind from system perspective deals with high dimensional complex world as an adaptive Multi-Input Multi-Output complex system. This view is theorized by reductionism theory in philosophy of mind, where the world is represented as logical combination of simpler sub-systems for human so that operate with less energy. On the other hand, Human usually uses linguistic rules to describe and manipulate his expert knowledge about the world; the way that is well modeled by Fuzzy Logic. But how such a symbolic form of knowledge can be encoded and stored in plausible neural circuitry? Based on mentioned postulates, we have proposed an adaptive Neuro-Fuzzy machine in order to model a rule-based MIMO system as logical combination of spatially distributed Single-Input Single-Output sub-systems. Each SISO systems as sensory and processing layer of the inference system, construct a single rule and learning process is handled by a Hebbian-like Spike-Time Dependent Plasticity. To shape a concrete knowledge about the whole system, extracted features of SISO neural systems (or equivalently the rules associated with SISO systems) are combined. To exhibit the system applicability, a single link cart-pole balancer as a sensory-motor learning task, has been simulated. The system is provided by reinforcement feedback from environment and is able to learn how to get expert and achieve a successful policy to perform motor control.

Abstract Human Brain is one of the most wonderful and complex systems which is designed for ever; A huge complex network composed of neurons as tiny biological and chemical processors which are distributed and work together as a super parallel system to do control and vital activities of human body. Brain learning simulation and hardware implementation is one of the most interesting research areas in order to make artificial brain. One of the researches in this area is Active Learning Method in brief ALM.

In systems with hysteresis behavior like Shape Memory Alloy (SMA) actuators and Piezo actuators, an accurate modeling of hysteresis behavior either for performance evaluation and identification or controller design is essentially needed. One of the most interesting hysteresis none-linearity identification methods is Preisach model which the hysteresis is modeled by linear combination of hysteresis operators.

Active Learning Method (ALM) is a powerful fuzzy soft computing tool, developed originally in order to promote an engineering realization of human brain. This algorithm, as a macro-level brain imitation, has been inspired by some behavioral specifications of human brain and active learning ability. ALM is an adaptive recursive fuzzy learning algorithm, in which a complex Multi Input, Multi Output system can be represented as a fuzzy combination of several Single-Input, Single-Output systems.

Abstract This paper presents an effective hybrid optimal modulation technique for a cascaded H-bridge (CHB) multilevel inverter. The introduced method is generalized optimal pulsewidth modulation, which can be extended to any $ m $-level CHB multilevel inverter operating at any switching frequency. The presented hybrid optimal modulation strategy includes the pattern exchange and optimal modulation techniques.

Abstract In systems with hysteresis behavior like magnetic cores, Piezo actuators, Shape Memory Alloy (SMA), we essentially need an accurate modeling of hysteresis either for design or performance evaluation; also in some control applications accurate system identification is needed. One of the famous methods of Hysteresis modeling is Preisach model. In this numerical method hysteresis is modeled by linear combination of smaller hysteresis loops as an elemental operator and local memory.

Preisach model is a well-known hysteresis identification method in which the hysteresis is modeled by linear combination of hysteresis operators. Although Preisach model describes the main features of system with hysteresis behavior, due to its rigorous numerical nature, it is not convenient to use in real-time control applications.

Summary Simulation and hardware implementation of human brain learning mechanism as centroid of human intelligence, is one of the most interesting research areas in order to make artificial brain and exploit human brain abilities. Active Learning Method in brief ALM is one of the accomplished researches in this field. ALM is an adaptive recursive fuzzy learning algorithm which is inspired by some behavioral features of human brain functionality and brain learning process.

Summary: Human brain is one of the most wonderful complex machines which are designed for ever. A huge complex network consists of neurons as tiny biological and chemical processors which are distributed and work together as a super parallel system to do control vital activities of human body.