scholarly journals Using the modified k-mean algorithm with an improved teaching-learning-based optimization algorithm for feedforward neural network training

2021 ◽  
Vol 11 (6) ◽  
pp. 5277
Author(s):  
Morteza Jouyban ◽  
Mahdie Khorashadizade
Keyword(s):  
Neural Network ◽  
Global Optimum ◽  
Feedforward Neural Network ◽  
Initial Population ◽  
Training Algorithm ◽  
Optimization Strategy ◽  
Basic Algorithm ◽  
Starting Point ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

In this paper we proposed a novel procedure for training a feedforward neural network. The accuracy of artificial neural network outputs after determining the proper structure for each problem depends on choosing the appropriate method for determining the best weights, which is the appropriate training algorithm. If the training algorithm starts from a good starting point, it is several steps closer to achieving global optimization. In this paper, we present an optimization strategy for selecting the initial population and determining the optimal weights with the aim of minimizing neural network error. Teaching-learning-based optimization (TLBO) is a less parametric algorithm rather than other evolutionary algorithms, so it is easier to implement. We have improved this algorithm to increase efficiency and balance between global and local search. The improved teaching-learning-based optimization (ITLBO) algorithm has added the concept of neighborhood to the basic algorithm, which improves the ability of global search. Using an initial population that includes the best cluster centers after clustering with the modified k-mean algorithm also helps the algorithm to achieve global optimum. The results are promising, close to optimal, and better than other approach which we compared our proposed algorithm with them.

A New Optimization Strategy for Chemical Mechanical Polishing Process

2000 ◽  
Author(s):  
Gou-Jen Wang ◽  
Jau-Liang Chen ◽  
Ju-Yi Hwang
Keyword(s):  
Neural Network ◽  
Taguchi Method ◽  
Signal To Noise Ratio ◽  
Global Optimum ◽  
Optimum Process ◽  
Local Optimum ◽  
Optimization Strategy ◽  
Two Stage ◽  
Chemical Mechanical Polishing Process ◽  
Trained Neural Network

Abstract In this paper, a systematic approach to achieve global optimum CMP process is carried out. In this new approach, orthogonal array technique adopted from the Taguchi method is used for efficient experiment design. The neural network (NN) technique is then applied to model the complex CMP process. Signal to Noise Ratio (S/N) Analysis (ANOVA) technique used in the conventional Taguchi method is also implemented to obtain the local optimum process parameters. Successively, the global optimum parameters are acquired in terms of the trained neural network. In order to increase the CMP throughput, a two-stage optimal strategy is also proposed. Experimental results demonstrate that the two-stage strategy can perform better then the original approach even though the polishing time is reduced by 1/6.

Evolutionary Learning of a Box-Pushing Controller

2011 ◽  
pp. 104-121 ◽  
Author(s):  
Pieter Spronck ◽  
Ida Sprinkhuizen-Kuyper ◽  
Eric Postma ◽  
Rens Kortmann
Keyword(s):  
Neural Network ◽  
Evolutionary Algorithm ◽  
Fitness Function ◽  
Research Question ◽  
Initial Population ◽  
Evolutionary Learning ◽  
Main Research ◽  
The Neural Network ◽  
Starting Point ◽  
Good Starting Point

In our research we use evolutionary algorithms to evolve robot controllers for executing elementary behaviours. This chapter focuses on the behaviour of pushing a box between two walls. The main research question addressed in this chapter is: how can a neural network learn to control the box-pushing task using evolutionary-computation techniques? In answering this question we study the following three characteristics by means of simulation experiments: (1) the fitness function, (2) the neural network topology and (3) the parameters of the evolutionary algorithm. We find that appropriate choices for these characteristics are: (1) a global external fitness function, (2) a recurrent neural network, and (3) a regular evolutionary algorithm augmented with the doping technique in which the initial population is supplied with a solution to a hard task instance. We conclude by stating that our findings on the relatively simple box-pushing behaviour form a good starting point for the evolutionary learning of more complex behaviours.

Modified Teaching-Learning-Based Optimization for Combined Heat and Power Economic Dispatch

2017 ◽  
Vol 18 (5) ◽  
Author(s):  
Jagat Kishore Pattanaik ◽  
Mousumi Basu ◽  
Deba Prasad Dash
Keyword(s):  
High Probability ◽  
Economic Dispatch ◽  
Combined Heat And Power ◽  
Global Optimum ◽  
Test Results ◽  
Suggested Technique ◽  
Teaching Learning Based Optimization ◽  
Thermal Generator ◽  
Teaching Learning ◽  
Power Economic

AbstractThis paper develops and suggests modified teaching-learning-based optimization to solve non-smooth non-convex combined heat and power economic dispatch (CHPED) problem. The valve-point loading and forbidden working regions of conventional thermal generator are taken into account. Modified teaching–learning-based optimization (MTLBO) introduces Gaussian random variables in the ‘Teacher phase’ and ‘Learner phase’ which perks up search efficiency and assurances a high probability of acquiring the global optimum without considerably impairing the convergence speed and the simplicity of the structure of teaching-learning-based optimization (TLBO). The efficacy of the suggested MTLBO technique has been confirmed on three test systems and 15 benchmark functions. Test results of the suggested technique have been matched up to those achieved by other evolutionary techniques. It has been observed from the comparison that the suggested MTLBO technique has the ability to endow with superior solution.

scholarly journals Heart Disease Diagnosis Utilizing Hybrid Fuzzy Wavelet Neural Network and Teaching Learning Based Optimization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jamal Salahaldeen Majeed Alneamy ◽  
Rahma Abdulwahid Hameed Alnaish
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Disease Diagnosis ◽  
Wavelet Neural Network ◽  
Superior Performance ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Fuzzy Wavelet Neural Network ◽  
Heart Disease Diagnosis ◽  
Teaching Learning

Among the various diseases that threaten human life is heart disease. This disease is considered to be one of the leading causes of death in the world. Actually, the medical diagnosis of heart disease is a complex task and must be made in an accurate manner. Therefore, a software has been developed based on advanced computer technologies to assist doctors in the diagnostic process. This paper intends to use the hybrid teaching learning based optimization (TLBO) algorithm and fuzzy wavelet neural network (FWNN) for heart disease diagnosis. The TLBO algorithm is applied to enhance performance of the FWNN. The hybrid TLBO algorithm with FWNN is used to classify the Cleveland heart disease dataset obtained from the University of California at Irvine (UCI) machine learning repository. The performance of the proposed method (TLBO_FWNN) is estimated using K-fold cross validation based on mean square error (MSE), classification accuracy, and the execution time. The experimental results show that TLBO_FWNN has an effective performance for diagnosing heart disease with 90.29% accuracy and superior performance compared to other methods in the literature.

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