New Evolutionary Neural Network Based on Continuous Ant Colony Optimization

2011 ◽  
Vol 58-60 ◽  
pp. 1773-1778
Author(s):  
Wei Gao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Ant Colony Optimization ◽  
Bp Neural Network ◽  
Evolutionary Programming ◽  
Ant Colony ◽  
Evolutionary Neural Network ◽  
Evolutionary Neural Networks ◽  
Xor Problem

The evolutionary neural network can be generated combining the evolutionary optimization algorithm and neural network. Based on analysis of shortcomings of previously proposed evolutionary neural networks, combining the continuous ant colony optimization proposed by author and BP neural network, a new evolutionary neural network whose architecture and connection weights evolve simultaneously is proposed. At last, through the typical XOR problem, the new evolutionary neural network is compared and analyzed with BP neural network and traditional evolutionary neural networks based on genetic algorithm and evolutionary programming. The computing results show that the precision and efficiency of the new neural network are all better.

Optimized Hybridization of Ant Colony Optimization and Genetic Algorithm (HACOGA) Based Interpretable Intuitive and Correlated-Contours Fuzzy Neural Network Classifier for Abalone

2020 ◽  
Vol 17 (6) ◽  
pp. 2755-2762
Author(s):  
Pramoda Patro ◽  
Krishna Kumar ◽  
G. Suresh Kumar
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Optimization Problem ◽  
Fuzzy Neural Network ◽  
Ant Colony ◽  
Fuzzy Rules ◽  
Fuzzy Neural ◽  
Approximate Function ◽  
Age Prediction

Classification generally assigns objects to enormous predefined categories and it is pervasive crisis that covers various application. Preparing the data for Classification and Prediction is the major problem in classification. In order to rectify this issue, an approximate function is proposed using Interpretable intuitive and Correlated-contours Fuzzy Neural Network (IC-FNN). For acquiring cor- related fuzzy rules and non-separable rules that comes under proper optimization problem. The extracted fuzzy rule’s parameter was fine-tuned sourced on hierarchical Levenberg Marquardt (LM) learning method for enhancing performance. But here parameters of fuzzy rules aren’t tuned per- fectly. Hybridization of Ant Colony Optimization Genetic Algorithm (HACOGA) is proposed here to rectify these issues. It tunes the parameters of the extracted fuzzy rules. Hybridization is enforced to certain factors and ACO and GA variables that share same characteristics in the computation. Experimental results shows that proposed HACOGA assist in enhancing the performance of FNN with recall, precision, accuracy and F -measure for the Abalone age prediction dataset.

Application of Hybrid Genetic Algorithm in Ground Stress Inversion

2011 ◽  
Vol 90-93 ◽  
pp. 337-341
Author(s):  
Ran Gang Yu ◽  
Yong Tian
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Local Minimum ◽  
Hybrid Genetic Algorithm ◽  
Inversion Method ◽  
Stress Inversion ◽  
Neural Network Inversion ◽  
Ground Stress

This paper propose genetic algorithm combined with neural networks, greatly improving the convergence rate of neural network aim at the disadvantage of the traditional BP neural network inversion method is easy to fall into local minimum and slow convergence.Finally, verified the feasibility and superiority of the above methods through the successful initial ground stress inversion of actual project.

Quality Prediction of Laser Cladding Based on Evolutionary Neural Network

2010 ◽  
Vol 44-47 ◽  
pp. 1012-1017
Author(s):  
Zhao Mei Xu ◽  
Hai Bing Wu ◽  
Zong Hai Hong
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Laser Cladding ◽  
Back Propagation ◽  
Maximum Relative Error ◽  
Technical Parameters ◽  
Evolutionary Neural Network ◽  
Real Value ◽  
Accuracy Of Prediction

Artificial neural networks were introduced in the area of laser cladding forming. The prediction model of surface quality in laser cladding parts, including the width, depth of cladding layer and dilution rate, was proposed based on the improved learned arithmetic. The model combined the global optimization searching performance of the genetic algorithm and localization of the back propagation(BP) neural networks. Five technical parameters were selected to test the reliability of the mode. The simulation and experimental results show that the evolutionary neural network based on genetic algorithm can effectively overcome the problem of falling into local minimum point. This method can get higher accuracy of prediction. It improves the measurement precision with the maximum relative error 2.14% between the predicted content and the real value.

scholarly journals Model Classification Of Nominal Value And The Original Of IDR Money By Applying Evolutionary Neural Network

2020 ◽  
Vol 3 (2) ◽  
pp. 258-265
Author(s):  
Al-Khowarizmi Al-Khowarizmi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Genetic Algorithms ◽  
Object Recognition ◽  
Success Rate ◽  
Model Classification ◽  
Evolutionary Neural Network ◽  
Evolutionary Neural Networks

Indonesian Rupiah (IDR) banknotes have unique characteristics that distinguish them from one another, both in the form of numbers, zeros and background images. This pattern of each type of banknote will be modeled in order to test the nominal value and authenticity of IDR, so as to be able to distinguish not only IDR banknotes but also other denominations. Evolutionary Neural Network is the development of the concept of evolution to get a neural network (NN) using genetic algorithms (GA). In this paper the application of evolutionary neural networks with less input is able to have a better success rate in object recognition, because the parameters for producing neural networks are far better

Research on the Prediction Method for Library Reader Flow Based on Evolutionary Neural Network

2014 ◽  
Vol 543-547 ◽  
pp. 2128-2132
Author(s):  
Ping Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Network Technology ◽  
Important Work ◽  
Evolutionary Neural Network ◽  
Flow Prediction ◽  
Risk Period ◽  
Neural Network Technology

It is an important work for modern libraries to predict reader flow. With the help of reader flow, library staff can grasp the change regulation of readers, allocate tasks rationally and take steps ahead of time in high-risk period. Because of reader flows typical non-linear characteristics, evolutionary neural network technology is introduced in this research so as to improve the accuracy of reader flow prediction. A prediction method for library reader flow based on evolutionary neural network is proposed. Genetic algorithm is used to optimize and design BP neural network firstly, then evolutionary neural network is used to predict reader flow. The experimental results show that evolutionary neural network is an effective tool for us to predict library reader flow. We can realize an accurate prediction for library reader flow by this method.

Ant Colony Optimization Applied to the Training of a High Order Neural Network with Adaptable Exponential Weights

2020 ◽  
pp. 82-95
Author(s):  
Ashraf M. Abdelbar ◽  
Islam Elnabarawy ◽  
Donald C. Wunsch II ◽  
Khalid M. Salama
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Ant Colony Optimization ◽  
High Order ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Test Set ◽  
Benchmark Datasets ◽  
Gradient Based ◽  
High Order Neural Network

High order neural networks (HONN) are neural networks which employ neurons that combine their inputs non-linearly. The HONEST (High Order Network with Exponential SynapTic links) network is a HONN that uses neurons with product units and adaptable exponents. The output of a trained HONEST network can be expressed in terms of the network inputs by a polynomial-like equation. This makes the structure of the network more transparent and easier to interpret. This study adapts ACOℝ, an Ant Colony Optimization algorithm, to the training of an HONEST network. Using a collection of 10 widely-used benchmark datasets, we compare ACOℝ to the well-known gradient-based Resilient Propagation (R-Prop) algorithm, in the training of HONEST networks. We find that our adaptation of ACOℝ has better test set generalization than R-Prop, though not to a statistically significant extent.

Ant Colony Optimization Applied to the Training of a High Order Neural Network with Adaptable Exponential Weights

2016 ◽  
pp. 362-374 ◽  
Author(s):  
Ashraf M. Abdelbar ◽  
Islam Elnabarawy ◽  
Donald C. Wunsch II ◽  
Khalid M. Salama
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Ant Colony Optimization ◽  
High Order ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Test Set ◽  
Benchmark Datasets ◽  
Gradient Based ◽  
High Order Neural Network

High order neural networks (HONN) are neural networks which employ neurons that combine their inputs non-linearly. The HONEST (High Order Network with Exponential SynapTic links) network is a HONN that uses neurons with product units and adaptable exponents. The output of a trained HONEST network can be expressed in terms of the network inputs by a polynomial-like equation. This makes the structure of the network more transparent and easier to interpret. This study adapts ACOR, an Ant Colony Optimization algorithm, to the training of an HONEST network. Using a collection of 10 widely-used benchmark datasets, we compare ACOR to the well-known gradient-based Resilient Propagation (R-Prop) algorithm, in the training of HONEST networks. We find that our adaptation of ACOR has better test set generalization than R-Prop, though not to a statistically significant extent.

Applied-Information Technology in Short-Term Wind Speed Forecast Model for Wind Farms Based on Ant Colony Optimization and BP Neural Network

2014 ◽  
Vol 662 ◽  
pp. 259-262 ◽  
Author(s):  
Qi Di Zhao ◽  
Yang Yu ◽  
Meng Meng Jia
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Ant Colony Optimization ◽  
Bp Neural Network ◽  
Ant Colony Algorithm ◽  
Back Propagation ◽  
Wind Farms ◽  
Forecast Model ◽  
Ant Colony ◽  
Short Term

To improve the short-term wind speed forecasting accuracy of wind farms, a prediction model based on back propagation (BP) neural network combining ant colony algorithm is built to predict short-term wind speed. The input variables of BP neural network predictive model are historical wind speeds, temperature, and air pressure. Ant colony algorithm is used to optimize the weights and bias of BP neural networks. Using the ant colony optimization BP neural network model to predict the future 1h wind speed, the simulation results show that the proposed method offers the advantages of high precision and fast convergence in contrast with BP neural network.

Study on Construction Bidding Management Based on GA-BP Neural Networks

2014 ◽  
Vol 584-586 ◽  
pp. 2423-2426
Author(s):  
Tian Bao Wu ◽  
Xun Liu ◽  
Tai Quan Zhou
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Bp Neural Network ◽  
Evaluation System ◽  
Construction Project ◽  
Basic Theory ◽  
Slow Convergence ◽  
Artificial Neural

In the bidding evaluation, the deviations are likely to be brought about by experts' subjectivity, arbitrary and tendentiousness. A method for construction project bidding based on the BP neural network improved by GA (Genetic Algorithm) is proposed. On the basis of the basic theory of the BP neural network, discussions are provided on how to rectify the drawbacks of slow convergence and prone to convergence to minimum with the use of GA. The model is successfully applied GA - BP artificial neural networks to project, which are in concert with the result of experts. The study makes contribution to research about the evaluation system of construction bidding management.

Sign in / Sign up

Export Citation Format

Share Document