Model Learning from Weights by Adaptive Enhanced Probabilistic Convergent Network
نویسندگان
چکیده
Current weightless classifiers require historical data to model a system and make prediction about a system successfully. Historical data either is not always available or does not take a recent system modification into consideration. For this reason an adaptive filter is designed, which when employed with a weightless classifier enables system model, difficult to characterise system model, and system output prediction, successfully. [email protected] Results of experiments performed show that the fusion of an adaptive filter and a weightless classifier is more beneficial than the filter or the classifier utilised singly, and that no speed advantage is observed.
منابع مشابه
The Time Adaptive Self Organizing Map for Distribution Estimation
The feature map represented by the set of weight vectors of the basic SOM (Self-Organizing Map) provides a good approximation to the input space from which the sample vectors come. But the timedecreasing learning rate and neighborhood function of the basic SOM algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changi...
متن کاملAn Analysis of Hardware Configurations for an Adaptive Weightless Neural Network
This paper examines the potential offered by adaptive hardware configurations of a class of weightless neural architecture called the Enhanced Probabilistic Convergent Network targeted on a Virtex-II pro FPGA which is re configurable. The reconfiguration and adaptive capability of the Enhanced Probabilistic Convergent Network is a highly adaptive architecture offering a very fast, automated, un...
متن کاملA Comparative Study of the Neural Network, Fuzzy Logic, and Nero-fuzzy Systems in Seismic Reservoir Characterization: An Example from Arab (Surmeh) Reservoir as an Iranian Gas Field, Persian Gulf Basin
Intelligent reservoir characterization using seismic attributes and hydraulic flow units has a vital role in the description of oil and gas traps. The predicted model allows an accurate understanding of the reservoir quality, especially at the un-cored well location. This study was conducted in two major steps. In the first step, the survey compared different intelligent techniques to discover ...
متن کاملFPGA-based enhanced probabilistic convergent weightless Network for human Iris recognition
This paper investigates how human identification and identity verification can be performed by the application of an FPGA based weightless neural network, entitled the Enhanced Probabilistic Convergent Neural Network (EPCN), to the iris biometric modality. The human iris is processed for feature vectors which will be employed for formation of connectivity, during learning and subsequent recogni...
متن کاملA Probabilistic Model of Learning Fields in Islamic Economics and Finance
In this paper an epistemological model of learning fields of probabilistic events is formalized. It is used to explain resource allocation governed by pervasive complementarities as the sign of unity of knowledge. Such an episteme is induced epistemologically into interacting, integrating and evolutionary variables representing the problem at hand. The end result is the formalization of a p...
متن کامل