Resource constrained design of artificial neural networks

Wah, Benjamin W.; Kriplani, Himanshu

doi:10.1109/ijcnn.1990.137856

Cited by 7 publications

(2 citation statements)

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“…To our knowledge, two other systems tackle the problem of autonomous ANN design process. First, Wah and Kriplani (1990) use an expert system to automate the process of standard backpropagation design. The expert system is used as a search mechanism to find the best learning configuration from the set of possible design parameters given by the user.…”

Section: Autonomous Ann Design Process Supervised By Neurex ~mentioning

confidence: 99%

Autonomous Design of Artificial Neural Networks by Neurex

Michaud

Rubio

1996

Neural Computation

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Artificial neural networks (ANN) have been demonstrated to be increasingly more useful for complex problems difficult to solve with conventional methods. With their learning abilities, they avoid having to develop a mathematical model or acquiring the appropriate knowledge to solve a task. The difficulty now lies in the ANN design process. A lot of choices must be made to design an ANN, and there are no available design rules to make these choices directly for a particular problem. Therefore, the design for an ANN demands a certain number of iterations, mainly guided by the expertise and the intuition of the developer. To automate the ANN design process, we have developed Neurex, composed of an expert system and an ANN simulator. Neurex autonomously guides the iterative ANN design process. Its structure tries to reproduce the design steps done by a human expert in conceiving an ANN. As a whole, the Neurex structure serves as a framework to implement this expertise for different learning paradigms. This article presents the system's general characteristics and its use in designing ANN using the standard backpropagation learning law.

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Section: Autonomous Ann Design Process Supervised By Neurex ~mentioning

confidence: 99%

Autonomous Design of Artificial Neural Networks by Neurex

Michaud

Rubio

1996

Neural Computation

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“…Population-based learning has been found to be a viable alternative to SCA, and its applicability is characterized mainly by the nature of its episodes. It has been used for learning new strategies for static load balancing of dependent jobs [92,64], as well as for dynamic load balancing of independent jobs [93], and for designing suitable neural-network configurations [94,95] …”

Section: Example 3 (Cont'd)mentioning

confidence: 99%

Strategy Learning: A Survey of Problems, Methods, and Architectures

Mehra

Wah

1998

Int. J. Artif. Intell. Tools

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Problem solvers employ strategies in searching for solutions to given problem instances. Strategies have traditionally been designed by experts using prior knowledge and refined manually using trial and error. Recent attempts to automate these processes have produced strategy-learning systems. This paper shows how various issues in strategy learning are affected by the nature of performance tasks, problem solvers, and learning environments. Surveyed learning systems are grouped by the commonality of their approaches into four general architectures.

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