FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W29269780> ?p ?o ?g. }

• W29269780 abstract "Learning an Artificial Neural Network (ANN) is an optimization task since it isdesirable to find optimal weight sets of an ANN in the training process. Differentequations are used to guide the network for providing an accurate result with lesstraining and testing error. Most of the training algorithms focus on weight values,activation functions, and network structures for providing optimal outputs.Backpropagation (BP) learning algorithm is the well-known learning technique thattrained ANN. However, some difficulties arise where the BP cannot get achievementswithout trapping in local minima and converge very slow in the solution space. Therefore, to overcome the trapping difficulties, slow convergence and difficulties in finding optimal weight values, three improved Artificial Bee Colony (ABC) algorithmsbuilt on the social insect behavior are proposed in this research for training ANN,namely the widely used Multilayer Perceptron (MLP). Here, three improved learningapproaches inspired by artificial honey bee's behavior are used to train MLP. They are:Global Guided Artificial Bee Colony (GGABC), Improved Gbest Guided Artificial BeeColony (IGGABC) and Artificial Smart Bee Colony (ASBC) algorithm. These improved algorithms were used to increase the exploration, exploitation and keep them balance for getting optimal results for a given task. Furthermore, here these algorithms used to trainthe MLP on two tasks; the seismic event's prediction and Boolean function classification. The simulation results of the MLP trained with improved algorithms werecompared with that when trained with the standard BP, ABC, Global ABC and Particle Swarm Optimization algorithm. From the experimental analysis, the proposed improved algorithms get better the classification efficacy for time series prediction and Boolean function classification. Moreover, these improved algorithm's success to get high accuracy and optimize the best network's weight values for training the MLP." @default.
• W29269780 created "2016-06-24" @default.
• W29269780 creator A5045970551 @default.
• W29269780 date "2014-03-01" @default.
• W29269780 modified "2023-09-27" @default.
• W29269780 title "An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction" @default.
• W29269780 cites W102221807 @default.
• W29269780 cites W1053280221 @default.
• W29269780 cites W117507182 @default.
• W29269780 cites W12783489 @default.
• W29269780 cites W1487370128 @default.
• W29269780 cites W1492640216 @default.
• W29269780 cites W1495051919 @default.
• W29269780 cites W1498436455 @default.
• W29269780 cites W150571132 @default.
• W29269780 cites W1507040636 @default.
• W29269780 cites W1507823785 @default.
• W29269780 cites W152181749 @default.
• W29269780 cites W1525097444 @default.
• W29269780 cites W1526415548 @default.
• W29269780 cites W1529965533 @default.
• W29269780 cites W1531857721 @default.
• W29269780 cites W1537653938 @default.
• W29269780 cites W153931484 @default.
• W29269780 cites W1542787930 @default.
• W29269780 cites W1554663460 @default.
• W29269780 cites W1573676079 @default.
• W29269780 cites W1576307993 @default.
• W29269780 cites W1599455811 @default.
• W29269780 cites W1776866349 @default.
• W29269780 cites W1832758977 @default.
• W29269780 cites W1915363084 @default.
• W29269780 cites W1917273024 @default.
• W29269780 cites W1927063004 @default.
• W29269780 cites W1967793991 @default.
• W29269780 cites W1970271540 @default.
• W29269780 cites W1970623953 @default.
• W29269780 cites W1971446863 @default.
• W29269780 cites W1975201621 @default.
• W29269780 cites W1977033623 @default.
• W29269780 cites W1978549074 @default.
• W29269780 cites W1980462675 @default.
• W29269780 cites W1980948177 @default.
• W29269780 cites W1981646714 @default.
• W29269780 cites W1981976602 @default.
• W29269780 cites W1984121966 @default.
• W29269780 cites W1989906353 @default.
• W29269780 cites W1993912260 @default.
• W29269780 cites W1994167151 @default.
• W29269780 cites W1995341919 @default.
• W29269780 cites W1999250038 @default.
• W29269780 cites W2004280321 @default.
• W29269780 cites W2007239252 @default.
• W29269780 cites W2007757881 @default.
• W29269780 cites W2012595795 @default.
• W29269780 cites W2012893126 @default.
• W29269780 cites W2018571946 @default.
• W29269780 cites W2018704488 @default.
• W29269780 cites W2020236162 @default.
• W29269780 cites W2020516950 @default.
• W29269780 cites W2021641789 @default.
• W29269780 cites W2023519762 @default.
• W29269780 cites W2027407435 @default.
• W29269780 cites W2032762054 @default.
• W29269780 cites W2035405290 @default.
• W29269780 cites W2040870580 @default.
• W29269780 cites W2043833885 @default.
• W29269780 cites W2043968544 @default.
• W29269780 cites W2044643617 @default.
• W29269780 cites W2045069106 @default.
• W29269780 cites W2045239993 @default.
• W29269780 cites W2047626401 @default.
• W29269780 cites W2048571789 @default.
• W29269780 cites W2051115352 @default.
• W29269780 cites W2054561910 @default.
• W29269780 cites W2056600751 @default.
• W29269780 cites W2056636221 @default.
• W29269780 cites W2058941342 @default.
• W29269780 cites W2061518278 @default.
• W29269780 cites W2062783952 @default.
• W29269780 cites W2063395121 @default.
• W29269780 cites W2063714349 @default.
• W29269780 cites W2071650788 @default.
• W29269780 cites W2072259788 @default.
• W29269780 cites W2072350446 @default.
• W29269780 cites W2072843675 @default.
• W29269780 cites W2078721389 @default.
• W29269780 cites W2079143456 @default.
• W29269780 cites W2079951033 @default.
• W29269780 cites W2081631884 @default.
• W29269780 cites W2081749411 @default.
• W29269780 cites W2082188830 @default.
• W29269780 cites W2082576747 @default.
• W29269780 cites W2083060785 @default.
• W29269780 cites W2084570114 @default.
• W29269780 cites W2086789740 @default.
• W29269780 cites W2090689364 @default.
• W29269780 cites W2090829771 @default.
• W29269780 cites W2091085232 @default.
• W29269780 cites W2092082234 @default.

• next