A unified tool for fuzzy/neural network systems

1st International Symposium on Neuro-Fuzzy Systems, AT '96. Conference Report, 1996

1 This paper describes DANIELA a Neuro-Fuzzy system for control applications. The system is based on a custom neural device that can implement either Multi-Layer Perceptrons, Radial Basis Functions or Fuzzy paradigms. The system implements intelligent control algorithms mixing neuro-fuzzy paradigms with nite state automatas and is used to control a walking hexapod.

Proceedings of the 6th International Conference on Frontiers of Information Technology - FIT '09, 2009

The complexity of technical systems in automation is rapidly growing and is causing increasing costs. Research has to identify appropriate measures to deal with the increasing complexity; Artificial Intelligence (AI) and Cognitive Science (CS) already show noteworthy results. However, compared to human intelligence those results can only be valued as modest. In the following the authors will try to verify the necessity of developing new principles to model control systems and will try to show, which technical principles and tools are suitable for the defined requirements. However, the challenge will be to accept a new kind of thinking in the bionic world, which will be in accord with scientists of other specific scientific faculties such as neuropsychoanalysis. The goal must be a formal approach in specifying the psyche. 1

1996

In the last few years the applications of artificial intelligence techniques have been used to convert human experience into a form understandable by computers. Advanced control based on artificial intelligence techniques is called intelligent control. Intelligent systems are usually described by analogies with biological systems by, for example, looking at how human beings perform control tasks, recognize patterns, or make decisions. There exists a mismatch between humans and machines: humans reason in uncertain, imprecise, fuzzy ways while machines and the computers that run them are based on binary reasoning. Fuzzy logic is a way to make machines more intelligent enabling them to reason in a fuzzy manner like humans. Fuzzy logic, proposed by Lotfy Zadeh in 1965, emerged as a tool to deal with uncertain, imprecise, or qualitative decision-making problems. Controllers that combine intelligent and conventional techniques are commonly used in the intelligent control of complex dynamic systems. Therefore, embedded fuzzy controllers automate what has traditionally been a human control activity.

Technological Forecasting and Social Change, 1991

Development of an expert system is difficult because of two challenges involve in it. The first one is the expert system itself is high level system and deals with knowledge, which make is difficult to handle. Second, the systems development is more art and less science; hence there are little guidelines available about the development. This paper describes computer aided development of intelligent systems using modem artificial intelligence technology. The paper illustrates a design of a reusable generic framework to support friendly development of fuzzy, neural network and hybrid systems such as neuro-fuzzy system. The reusable component libraries for fuzzy logic based systems, neural network based system and hybrid system such as neuro-fuzzy system are developed and accommodated in this framework. The paper demonstrates code snippets, interface screens and class libraries overview with necessary technical details.

Acta Polytechnica, 2004

Artificial neural networks and genetic algorithms are often quoted in discussions about the contribution of biological systems thinking in engineering design. This paper reviews work on the neuromuscular system, a field in which biological systems thinking could make specific contributions to the development and design of automatic control systems for mechatronics and robotics applications. The paper suggests some specific areas in which a better understanding of this biological control system could be expected to contribute to ...

1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century, 1995

Ras, 1997

2012

This paper presents a model of a control system for robot systems inspired by the functionality and organisation of human neuroregulatory system. Our model was specified using software agents within a formal framework and implemented through Web Services. This approach allows the implementation of the control logic of a robot system with relative ease, in an incremental way, using the addition of new control centres to the system as its behaviour is observed or needs to be detailed with greater precision, without the need to modify existing functionality. The tests performed verify that the proposed model has the general characteristics of biological systems together with the desirable features of software, such as robustness, flexibility, reuse and decoupling.