Abstract
The paper describes the operation principles of the evolutive neuro fuzzy filtering (ENFF) properties, which based on back propagation fuzzy neural net, this filter adaptively choose and emit a decision according with the reference signal changes of an external reference process, in order to actualize the best correct new conditions updating a process. This neural net fuzzy filter mechanism selects the best parameter values into the knowledge base (KB), to update the filter weights giving a good enough answers in accordance with the reference signal in natural sense. The filter architecture includes a decision making stage using an inference into its structure to deduce the filter decisions in accordance with the previous and actual filter answer in order to updates the new decision with respect to the new reference system con-ditions. The filtering process states require that bound into its own time limit as real time system, considering the Ny-quist and Shannon criteria. The characterization of the membership functions builds the knowledge base in probabilis-tic sense with respect to the rules set inference to describe the reference system and deduce the new filter decision, per-forming the ENFF answers. Moreover, the paper describes schematically the neural net architecture and the deci-sion-making stages in order to integrate them into the filter architecture as intelligent system. The results expressed in formal sense use the concepts into the paper references with a simulation of the ENFF into a Kalman filter structure using the Matlab© tool.
Highlights
The development of new intelligent systems requires mechanism that deduces its own external environment
The paper describes the operation principles of the evolutive neuro fuzzy filtering (ENFF) properties, which based on back propagation fuzzy neural net, this filter adaptively choose and emit a decision according with the reference signal changes of an external reference process, in order to actualize the best correct new conditions updating a process
The filter architecture includes a decision making stage using an inference into its structure to deduce the filter decisions in accordance with the previous and actual filter answer in order to updates the new decision with respect to the new reference system conditions
Summary
The development of new intelligent systems requires mechanism that deduces its own external environment. The neural net filter stage [3] works as a parallel fuzzy neurons in loop form, which has an iterative searching methodology used for evolutive algorithms and based on the back propagation (BP) algorithm since its parameters are updated dynamically ([4,5,6]) at each iteration by degrees [7] This process refers to a back propagation parameter adaptation [8], using supervised learning by the knowledge base according with the error e(k) ([9,10]) described by the difference between the desired response y(k) and the actual signal y k [11]1. The paper integrates the ENFF concept with its real time restrictions [9], using statistical methods in order to characterize the Kalman filter internal structure to give answers with respect to the operation levels in natural way making a specific decision in order to follow the natural reference model ([12,13])
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