Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Comparison of three classification techniques, CART, C4.5 and multi-layer perceptrons
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
C4.5: programs for machine learning
C4.5: programs for machine learning
Computational intelligence PC tools
Computational intelligence PC tools
Particle Systems—a Technique for Modeling a Class of Fuzzy Objects
ACM Transactions on Graphics (TOG)
Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Inductive Learning Algorithms for Complex Systems Modeling
Inductive Learning Algorithms for Complex Systems Modeling
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Particle Swarm Optimized Polynomials for Data Classification
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
MIMO CMAC neural network classifier for solving classification problems
Applied Soft Computing
A condensed polynomial neural network for classification using swarm intelligence
Applied Soft Computing
Research of neural network classifier based on FCM and PSO for breast cancer classification
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Accelerating FCM neural network classifier using graphics processing units with CUDA
Applied Intelligence
Hi-index | 0.10 |
It has been found that in solving classification task, the polynomial neural network (PNN) needs more computation time, as the partial descriptions (the heart of PNN) in each layer grow very fast. At the same time the complexity of the network also increases as the number of layers increases. In this context we propose a reduced and comprehensible polynomial neural network (RCPNN) for the task of classification for which partial descriptions have been developed only for a single layer of the PNN architecture and the output of these partial descriptions along with the features are fed to the output layer of the RCPNN having only one neuron. The weights between hidden layer and output layer have optimized by two different methods such as gradient descent and particle swarm optimization (PSO). A comparative performance in terms of computational cost and accuracy of PSO trained RCPNN and non-PSO (i.e. gradient descent) trained RCPNN with PNN has been given to prove the same. Our experimental results show that the performance in terms of cost and accuracy of the proposed RCPNN trained with PSO and gradient decent is more efficient than the PNN model.