Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence

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Analysis and Comparison of the Structure and Performance of Local Neural Networks

The paper synthesizes the local neural networks. Network structures and their activation functions of three local networks CMAC, B-spline, RBF that are often used to approach functions are analyzed and compared in detail. The network structure of ART-2 is also discussed. Based on the fuzzy system of these local networks, the paper depicts their fuzzy structures and performances. The study and analysis in the paper are useful to instruct to select and design the local neural networks

Use of human gestures for controlling a mobile robot via adaptive CMAC network and fuzzy logic controller

Mobile robots with manipulators have been more and more commonly applied in extreme and hostile environments to assist or even replace human operators for complex tasks. In addition to autonomous abilities, mobile robots need to facilitate the human–robot interaction control mode that enables human users to easily control or collaborate with robots. This paper proposes a system which uses human gestures to control an autonomous mobile robot integrating a manipulator and a video surveillance platform. A human user can control the mobile robot just as one drives an actual vehicle in the vehicle’s driving cab. The proposed system obtains human’s skeleton joints information using a motion sensing input device, which is then recognized and interpreted into a set of control commands. This is implemented, based on the availability of training data set and requirement of in-time performance, by an adaptive cerebellar model articulation controller neural network, a finite state machine, a fuzzy controller and purposely designed gesture recognition and control command generation systems. These algorithms work together implement the steering and velocity control of the mobile robot in real-time. The experimental results demonstrate that the proposed approach is able to conveniently control a mobile robot using virtual driving method, with smooth manoeuvring trajectories in various speeds

Intelligent flight control systems

The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms

A neural network architecture for implementation of expert systems for real time monitoring

Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered

Hazardous Odor Recognition by CMAC Based Neural Networks

Electronic noses are being developed as systems for the automated detection and classification of odors, vapors, and gases. Artificial neural networks (ANNs) have been used to analyze complex data and to recognize patterns, and have shown promising results in recognition of volatile compounds and odors in electronic nose applications. When an ANN is combined with a sensor array, the number of detectable chemicals is generally greater than the number of unique sensor types. The odor sensing system should be extended to new areas since its standard style where the output pattern from multiple sensors with partially overlapped specificity is recognized by a neural network or multivariate analysis. This paper describes the design, implementation and performance evaluations of the application developed for hazardous odor recognition using Cerebellar Model Articulation Controller (CMAC) based neural networks