Paper Info
Title
Pseudo Neural Networks Via Analytic Programming With Direct Coding Of Constant Estimation
Abstract
This research deals with a novel approach to classification - pseudo neural networks (PNN). This technique was inspired in classical artificial neural networks (ANN), where a relation between inputs and outputs is based on the mathematical transfer functions and optimised numerical weights. Compared to ANN, the whole structure in PNN, i.e. the relation between inputs and output(s), is fully synthesised by evolutionary symbolic regression tool - analytic programming. Compared to previous synthesised models, the PNN in this paper were synthesised via a new approach to constant estimation inside the analytic programming - direct coding. Iris data was used for the experiments and PNN were used for the synthesis of a complex classifier for more classes. For experimentation, Differential Evolution (de/rand/1/bin) for optimisation in analytic programming (AP) was used.
Year
DOI
Venue
2018
10.7148/2018-0143
32ND EUROPEAN CONFERENCE ON MODELLING AND SIMULATION (ECMS 2018)
Keywords
Field
DocType
Pseudo neural networks, Analytic programming, Differential evolution
Computer science,Algorithm,Coding (social sciences),Analytic programming,Artificial neural network
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Zuzana Kominkova Oplatkova18417.68
Adam Viktorin22916.76
Roman Senkerik337574.92