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 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.展开更多
This paper proposes a voice codification based on two algorithms that make the wave form codification in time domain. The first uses the significant impulse model (SIM), which has as a goal to operate as an endpoint d...This paper proposes a voice codification based on two algorithms that make the wave form codification in time domain. The first uses the significant impulse model (SIM), which has as a goal to operate as an endpoint detector and as a dawn sampling, through the detection and selection of the significant valleys and crests;the second algorithm, is a redundant wave-form recycler (RWR) that uses an architecture based on fuzzy logic with an accumulative memory. The fuzzy algorithm obtains the similitude grade between the redundant wave forms, this with the objective of save into an knowledge base the patterns, based on the no supervised learning and when there are into memory, automatically there will be used to identified their arrive respect to the input signal, substituting the input block by the correspondent pattern into memory. This decoding process is using the SIM interpolation with a memory in accordance to the RWR.展开更多
The paper makes a description of the fuzzy filter properties considering its operational principles. A digital filter interacts with a reference model signal into real process in order to get the best corresponding an...The paper makes a description of the fuzzy filter properties considering its operational principles. A digital filter interacts with a reference model signal into real process in order to get the best corresponding answer, having the minimum error at the filter output using the mean square criterion. Adding into this filter structure a fuzzy mechanism, to obtain an intelligent filtering because adaptively select and emit a decision answer according with the external reference signal changes, in order to actualize the best correct new conditions updating a process dynamically. The interpretation of the input signal level describes the operation of the reference model, to update the filter weights giving the answers approximation in accordance with the reference signal in natural form. Finally the paper shows the simulations results of the fuzzy filter into the Kalman structure using the Matlab? tool.展开更多
文摘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.
文摘This paper proposes a voice codification based on two algorithms that make the wave form codification in time domain. The first uses the significant impulse model (SIM), which has as a goal to operate as an endpoint detector and as a dawn sampling, through the detection and selection of the significant valleys and crests;the second algorithm, is a redundant wave-form recycler (RWR) that uses an architecture based on fuzzy logic with an accumulative memory. The fuzzy algorithm obtains the similitude grade between the redundant wave forms, this with the objective of save into an knowledge base the patterns, based on the no supervised learning and when there are into memory, automatically there will be used to identified their arrive respect to the input signal, substituting the input block by the correspondent pattern into memory. This decoding process is using the SIM interpolation with a memory in accordance to the RWR.
文摘The paper makes a description of the fuzzy filter properties considering its operational principles. A digital filter interacts with a reference model signal into real process in order to get the best corresponding answer, having the minimum error at the filter output using the mean square criterion. Adding into this filter structure a fuzzy mechanism, to obtain an intelligent filtering because adaptively select and emit a decision answer according with the external reference signal changes, in order to actualize the best correct new conditions updating a process dynamically. The interpretation of the input signal level describes the operation of the reference model, to update the filter weights giving the answers approximation in accordance with the reference signal in natural form. Finally the paper shows the simulations results of the fuzzy filter into the Kalman structure using the Matlab? tool.
