scholarly journals Integrated Computer Vision and Type-2 Fuzzy CMAC Model for Classifying Pilling of Knitted Fabric

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 367 ◽  
Author(s):  
Chin-Ling Lee ◽  
Cheng-Jian Lin
Keyword(s):  
Computer Vision ◽  
Artificial Bee Colony ◽  
Learning Algorithm ◽  
Local Optimum ◽  
Knitted Fabric ◽  
Morphological Operations ◽  
Cerebellar Model Articulation Controller ◽  
Bee Colony ◽  
Fuzzy Cmac

Human visual inspection for classifying the pilling of knitted fabric not only consumes human resources but also causes occupational hazard because of long-term observation using human eyes. This reduces the efficiency of the entire operation. To overcome this, an integrated computer vision and type-2 fuzzy cerebellar model articulation controller (T2FCMAC) was devised for classifying the pilling of knitted fabric. First, the fast Fourier transform was used for image preprocessing to strengthen the characteristics of the pilling in the fabric image. The background and the pilling of knitted fabric were then segmented through binary and morphological operations. Characteristics of the pilling on the fabric were extracted by using image topography. A novel T2FCMAC based on the hybrid of group strategy and artificial bee colony (HGSABC) was proposed to evaluate the pilling grade of knitted fabric. The proposed T2FCMAC classifier embedded a type-2 fuzzy system within a traditional cerebellar model articulation controller (CMAC). The proposed HGSABC learning algorithm was used for adjusting the parameters of T2FCMAC classifiers and preventing the fall into a local optimum. A group search strategy was used to obtain balanced search capabilities and improve the performance of the artificial bee colony algorithm. The experimental results of the fixed and different illuminations indicated that the proposed method exhibited a superior average accuracy (97.3% and 94.6%, respectively) to other methods.

scholarly journals Particle Swarm and Bacterial Foraging Inspired Hybrid Artificial Bee Colony Algorithm for Numerical Function Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Li Mao ◽  
Yu Mao ◽  
Changxi Zhou ◽  
Chaofeng Li ◽  
Xiao Wei ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Function Optimization ◽  
Local Optimum ◽  
Search Range ◽  
Bacterial Foraging Optimization ◽  
Numerical Function ◽  
Bacterial Foraging ◽  
Bee Colony

Artificial bee colony (ABC) algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC) algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.

scholarly journals A Binary Superior Tracking Artificial Bee Colony with Dynamic Cauchy Mutation for Feature Selection

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xianghua Chu ◽  
Shuxiang Li ◽  
Da Gao ◽  
Wei Zhao ◽  
Jianshuang Cui ◽  
...  
Keyword(s):  
Feature Selection ◽  
Artificial Bee Colony ◽  
Learning Strategy ◽  
Population Distribution ◽  
Learning Algorithm ◽  
Test Problems ◽  
Bee Colony ◽  
Cauchy Mutation ◽  
Optimal Classification ◽  
Improved Learning

This paper aims to propose an improved learning algorithm for feature selection, termed as binary superior tracking artificial bee colony with dynamic Cauchy mutation (BSTABC-DCM). To enhance exploitation capacity, a binary learning strategy is proposed to enable each bee to learn from the superior individuals in each dimension. A dynamic Cauchy mutation is introduced to diversify the population distribution. Ten datasets from UCI repository are adopted as test problems, and the average results of cross-validation of BSTABC-DCM are compared with other seven popular swarm intelligence metaheuristics. Experimental results demonstrate that BSTABC-DCM could obtain the optimal classification accuracy and select the best representative features for the UCI problems.

scholarly journals A Quick Gbest Guided Artificial Bee Colony Algorithm for Stock Market Prices Prediction

Symmetry ◽  
2018 ◽  
Vol 10 (7) ◽  
pp. 292 ◽  
Author(s):  
Habib Shah ◽  
Nasser Tairan ◽  
Harish Garg ◽  
Rozaida Ghazali
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Artificial Bee Colony ◽  
Learning Algorithm ◽  
Hybrid Technique ◽  
Market Prices ◽  
Market Values ◽  
Bee Colony ◽  
Global Issue ◽  
Investment Advisor

The objective of this work is to present a Quick Gbest Guided artificial bee colony (ABC) learning algorithm to train the feedforward neural network (QGGABC-FFNN) model for the prediction of the trends in the stock markets. As it is quite important to know that nowadays, stock market prediction of trends is a significant financial global issue. The scientists, finance administration, companies, and leadership of a given country struggle towards developing a strong financial position. Several technical, industrial, fundamental, scientific, and statistical tools have been proposed and used with varying results. Still, predicting an exact or near-to-exact trend of the Stock Market values behavior is an open problem. In this respect, in the present manuscript, we propose an algorithm based on ABC to minimize the error in the trend and actual values by using the hybrid technique based on neural network and artificial intelligence. The presented approach has been verified and tested to predict the accurate trend of Saudi Stock Market (SSM) values. The proposed QGGABC-ANN based on bio-inspired learning algorithm with its high degree of accuracy could be used as an investment advisor for the investors and traders in the future of SSM. The proposed approach is based mainly on SSM historical data covering a large span of time. From the simulation findings, the proposed QGGABC-FFNN outperformed compared with other typical computational algorithms for prediction of SSM values.

scholarly journals Chaotic Artificial Bee Colony Algorithm for System Identification of a Small-Scale Unmanned Helicopter

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Li Ding ◽  
Hongtao Wu ◽  
Yu Yao
Keyword(s):  
System Identification ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
State Space Model ◽  
Global Optimum ◽  
Small Scale ◽  
Local Optimum ◽  
Unmanned Helicopter ◽  
Bee Colony ◽  
Chaotic Operator

The purpose of this paper is devoted to developing a chaotic artificial bee colony algorithm (CABC) for the system identification of a small-scale unmanned helicopter state-space model in hover condition. In order to avoid the premature of traditional artificial bee colony algorithm (ABC), which is stuck in local optimum and can not reach the global optimum, a novel chaotic operator with the characteristics of ergodicity and irregularity was introduced to enhance its performance. With input-output data collected from actual flight experiments, the identification results showed the superiority of CABC over the ABC and the genetic algorithm (GA). Simulations are presented to demonstrate the effectiveness of our proposed algorithm and the accuracy of the identified helicopter model.

scholarly journals PENERAPAN ARTIFICIAL NEURAL NETWORK DENGAN OPTIMASI MODIFIED ARTIFICIAL BEE COLONY UNTUK MERAMALKAN HARGA BITCOIN TERHADAP RUPIAH

2020 ◽  
Vol 9 (2) ◽  
pp. 135-142
Author(s):  
Di Mokhammad Hakim Ilmawan ◽  
Budi Warsito ◽  
Sugito Sugito
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Bee Colony ◽  
Time Series Data ◽  
Learning Algorithm ◽  
Series Data ◽  
Future Period ◽  
Bee Colony ◽  
Artificial Neural ◽  
Digital Assets

Bitcoin is one of digital assets that can be used to make a profit. One of the ways to use Bitcoin profitly is to trade Bitcoin. At trade activities, decisions making whether to buy or not are very crucial. If we can predict the price of Bitcoin in the future period, we can make a decisions whether to buy Bitcoin or not. Artificial Neural Network can be used to predict Bitcoin price data which is time series data. There are many learning algorithm in Artificial Neural Network, Modified Artificial Bee Colony is one of optimization algorithm that used to solve the optimal weight of Artificial Neural Network. In this study, the Bitcoin exchage rate against Rupiah starting September 1, 2017 to January 4, 2019 are used. Based on the training results obtained that MAPE value is 3,12% and the testing results obtained that MAPE value is 2,02%. This represent that the prediction results from Artificial Neural Network optimized by Modified Artificial Bee Colony algorithm are quite accurate because of small MAPE value.

scholarly journals Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2250
Author(s):  
Mei Li ◽  
Gai-Ge Wang ◽  
Helong Yu
Keyword(s):  
Artificial Bee Colony ◽  
Flow Shop ◽  
Real Life ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Local Optimum ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Bee Colony ◽  
Mutation Strategy

In this era of unprecedented economic and social prosperity, problems such as energy shortages and environmental pollution are gradually coming to the fore, which seriously restrict economic and social development. In order to solve these problems, green shop scheduling, which is a key aspect of the manufacturing industry, has attracted the attention of researchers, and the widely used flow shop scheduling problem (HFSP) has become a hot topic of research. In this paper, we study the fuzzy hybrid green shop scheduling problem (FHFGSP) with fuzzy processing time, with the objective of minimizing makespan and total energy consumption. This is more in line with real-life situations. The non-linear integer programming model of FHFGSP is built by expressing job processing times as triangular fuzzy numbers (TFN) and considering the machine setup times when processing different jobs. To address the FHFGSP, a discrete artificial bee colony (DABC) algorithm based on similarity and non-dominated solution ordering is proposed, which allows individuals to explore their neighbors to different degrees in the employed bee phase according to a sequence of positions, increasing the diversity of the algorithm. During the onlooker bee phase, individuals at the front of the sequence have a higher chance of being tracked, increasing the convergence rate of the colony. In addition, a mutation strategy is proposed to prevent the population from falling into a local optimum. To verify the effectiveness of the algorithm, 400 test cases were generated, comparing the proposed strategy and the overall algorithm with each other and evaluating them using three different metrics. The experimental results show that the proposed algorithm outperforms other algorithms in terms of quantity, quality, convergence and diversity.

Sentiment Analysis on Microblogging with K-Means Clustering and Artificial Bee Colony

2019 ◽  
Vol 18 (03) ◽  
pp. 1950017 ◽  
Author(s):  
Korawit Orkphol ◽  
Wu Yang
Keyword(s):  
Artificial Bee Colony ◽  
Clustering Algorithm ◽  
Global Optimum ◽  
High Dimensional ◽  
Local Optimum ◽  
Short Message ◽  
Initial State ◽  
Bee Colony ◽  
Analysis Technique ◽  
Value Decomposition

Microblogging is a type of blog used by people to express their opinions, attitudes, and feelings toward entities with a short message and this message is easily shared through the network of connected people. Knowing their sentiments would be beneficial for decision-making, planning, visualization, and so on. Grouping similar microblogging messages can convey some meaningful sentiments toward an entity. This task can be accomplished by using a simple and fast clustering algorithm, [Formula: see text]-means. As the microblogging messages are short and noisy they cause high sparseness and high-dimensional dataset. To overcome this problem, term frequency–inverse document frequency (tf–idf) technique is employed for selecting the relevant features, and singular value decomposition (SVD) technique is employed for reducing the high-dimensional dataset while still retaining the most relevant features. These two techniques adjust dataset to improve the [Formula: see text]-means efficiently. Another problem comes from [Formula: see text]-means itself. [Formula: see text]-means result relies on the initial state of centroids, the random initial state of centroids usually causes convergence to a local optimum. To find a global optimum, artificial bee colony (ABC), a novel swarm intelligence algorithm, is employed to find the best initial state of centroids. Silhouette analysis technique is also used to find optimal [Formula: see text]. After clustering into [Formula: see text] groups, each group will be scored by SentiWordNet and we analyzed the sentiment polarities of each group. Our approach shows that combining various techniques (i.e., tf–idf, SVD, and ABC) can significantly improve [Formula: see text]-means result (41% from normal [Formula: see text]-means).

Sliding mode online learning algorithm for type-2 fuzzy CMAC networks

2013 ◽  
Author(s):  
Sevil Ahmed ◽  
Kostadin Shiev ◽  
Andon V. Topalov ◽  
Nikola Shakev ◽  
Okyay Kaynak
Keyword(s):  
Online Learning ◽  
Sliding Mode ◽  
Learning Algorithm ◽  
Fuzzy Cmac ◽  
Online Learning Algorithm

A Novel Interactively Recurrent Self-Evolving Fuzzy CMAC and Its Classification Applications

2015 ◽  
Vol 14 (03) ◽  
pp. 1550019
Author(s):  
Jyun-Guo Wang ◽  
Shen-Chuan Tai ◽  
Cheng-Jian Lin
Keyword(s):  
Structure Learning ◽  
Learning Algorithm ◽  
Descent Method ◽  
Superior Performance ◽  
Gradient Descent Method ◽  
Parameter Learning ◽  
Cerebellar Model Articulation Controller ◽  
Classification Problems ◽  
Fuzzy Cmac ◽  
Firing Strength

In this paper, an Interactively Recurrent Self-evolving Fuzzy Cerebellar Model Articulation Controller (IRSFCMAC) model is developed for solving classification problems. The proposed IRSFCMAC classifier consists of internal feedback and external loops, which are generated by the hypercube cell firing strength to itself and other hypercube cells. The learning process of the IRSFCMAC gets started with an empty hypercube base, and then all of hypercube cells are generated and learned online via structure and parameter learning, respectively. The structure learning algorithm is based on the degree measure to determine the number of hypercube cells. The parameter learning algorithm, based on the gradient descent method, adjusts the shapes of the membership functions and the corresponding fuzzy weights of the IRSFCMAC. Finally, the proposed IRSFCMAC model is tested by four benchmark classification problems. Experimental results show that the proposed IRSFCMAC model has superior performance than traditional FCMAC and other models.

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