S. Jakubek, N. Keuth:

"A new Training Algorithm for Neuro-Fuzzy Networks";

Vortrag: ICINCO 2005 (2nd internat. Conference on Informatics in Control, Automation and Robotics), Barcelona; 14.09.2005 - 17.09.2006; in: "Proceedings of the 2005 ICINCO", (2005).

In this paper a new iterative construction algorithm for local model

networks is presented. The algorithm is focussed on building models with sparsely

distributed data as they occur in engine optimization processes. The validity function

of each local model is fitted to the available data using statistical criteria

along with regularisation and thus allowing an arbitrary orientation and extent

in the input space. Local models are consecutively placed into those regions of

the input space where the model error is still large thus guaranteeing maximal

improvement through each new local model. The orientation and extent of each

validity function is also adapted to the available training data such that the determination

of the local regression parameters is a well posed problem. The regularisation

of the model can be controlled in a distinct manner using only two

user-defined parameters. Examples from an industrial problems illustrate the efficiency

of the proposed algorithm.

In this paper a new iterative construction algorithm for local model

networks is presented. The algorithm is focussed on building models with sparsely

distributed data as they occur in engine optimization processes. The validity function

of each local model is fitted to the available data using statistical criteria

along with regularisation and thus allowing an arbitrary orientation and extent

in the input space. Local models are consecutively placed into those regions of

the input space where the model error is still large thus guaranteeing maximal

improvement through each new local model. The orientation and extent of each

validity function is also adapted to the available training data such that the determination

of the local regression parameters is a well posed problem. The regularisation

of the model can be controlled in a distinct manner using only two

user-defined parameters. Examples from an industrial problems illustrate the efficiency

of the proposed algorithm.

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