Search Results - (( initial estimation learning algorithm ) OR ( using codification based algorithm ))
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Parameter estimation in computational systems biology models: a comparative study of initialization methods in global optimization
Published 2022“…In this work, several choices of initialization methods are compared and experimental results indicated that the algorithm is sensitive to the initial value of kinetic parameters. …”
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RECURSIVE LEARNING ALGORITHMS ON RBF NETWORKS FOR NONLINEAR SYSTEM IDENTIFICATION
Published 2010“…The thesis also try to investigate the influence of initialization of RBF weights parameters on the overall learning performance using random method and advanced unsupervised learning, such as clustering techniques, as a comparison. …”
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Harmony Search-Based Fuzzy Clustering Algorithms For Image Segmentation
Published 2011“…Fuzzy clustering algorithms, which fall under unsupervised machine learning, are among the most successful methods for image segmentation. …”
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A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction
Published 2021“…Crop yield predictions are carried out to estimate higher crop yield through the use of machine learning algorithms which are one of the challenging issues in the agricultural sector. …”
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Estimating the un-sampled ph value via neighbouring points using multi-layer neural network - genetic algorithm
Published 2023“…GA optimizer is used in MLNN-GA where the result of each learning weight will be the initial weight of the next learning process. …”
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Nonstationary signal reconstruction from TVAR coefficients
Published 2018“…The proposed method consists of three steps, where in the first step, initial values for TVAR coefficients are estimated from synaptic weights of a three layer Artificial Neural Network (ANN) which is trained using Backpropagation (BP) learning algorithm. …”
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Proceeding Paper -
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Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms
Published 2018“…Moreover, the balance between the exploration and exploitation processes in the DPSO framework is considered using a combination of (i) a kernel density estimation technique associated with new bandwidth estimation method and (ii) estimated multi-dimensional gravitational learning coefficients. …”
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Dynamic Bayesian Networks and Variable Length Genetic Algorithm for Dialogue Act Recognition
Published 2007“…Motivating by these drawbacks, this research proposes a new model of dialogue act recognition in which dynamic Bayesian machine learning is applied to induce dynamic Bayesian networks models from task-oriented dialogue corpus using sets of lexical cues selected automatically by means of new variable length genetic algorithm. …”
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Machine Learning Based Optimal Design of On-Road Charging Lane for Smart Cities Applications
Published 2025“…The algorithm not only aids in estimating the infrastructure cost of the charging lane but also predicts optimal design parameters using trained data. …”
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Evaluating Adan vs. Adam: an analysis of optimizer performance in deep learning
Published 2025“…Choosing a suitable optimization algorithm in deep learning is essential for effective model development as it significantly influences convergence speed, model performance, and the success of the train- ing process. …”
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Integration of machine learning and remote sensing for above ground biomass estimation through Landsat-9 and field data in temperate forests of the Himalayan region
Published 2024“…Through the utilization of openly accessible fine-resolution data and employing the RF algorithm, the research demonstrated promising outcomes in the identification of optimal predictor-algorithm combinations for forest AGB mapping. …”
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Feedback error learning control for underactuated acrobat robot with radial basis funtion based FIR filter
Published 2009“…Simulation results on a two link acrobat robot with nonzero initial angular momentum in achieving a final desired posture angle are presented to show the validity of the proposed algorithm.…”
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An extended adaptive mechanism of evolutionary based channel assignment via reinforcement
Published 2012“…The process of channel assignment must satisfy hard-constraints such as electromagnetic compatibility (EMC) and the demand of channels in a cell. Initial channel assignment parameters are obtained using self-learning scheme and evolutionary algorithms is used to fine-tune the estimated parameters from reinforcement learning algorithm to optimise the channel assignment problem in wireless mobile networks. …”
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Water wave optimization with deep learning driven smart grid stability prediction
Published 2022“…Since the entire procedure is valued on the basis of time, it is essential to perform adaptive estimation of the SG’s stability. Recent advancements in Machine Learning (ML) and Deep Learning (DL) models enable the designing of effective stability prediction models in SGs. …”
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Estimating Forest Aboveground Biomass Density Using Remote Sensing and Machine Learning : A RSME Approach
Published 2025“…This research illustrates the significance of combining different datasets and machine learning techniques for the remote assessment of forest biomass, thereby facilitating the improved modeling of ecosystem characteristics and sustainability initiatives. …”
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Predicting Diseases Using Multi-BackPropagation
Published 2002“…Multi-network approach does not require any changes in neural network learning algorithm. Instead, the large data is divided into several smaller categories or network. …”
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Backpropagation algorithm for classification problem: academic performance prediction model for UiTM Melaka Mengubah Destini Anak Bangsa (MDAB) program. / Fadhlina Izzah Saman, Nur...
Published 2012“…There is no existing tool to assist faculties in estimating the number of students that can achieve the objective, hence a prediction model using Backpropagation Algorithm is proposed by using a case study of UiTM Bandaraya Melaka Bachelor of Administrative Science students. …”
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Research Reports -
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Adaptive GRNN for the modelling of dynamic plants
Published 2002“…The results show that the proposed methodology is computationally efficient and exhibits several attractive features such as fast learning, flexible network sizing and good robustness, which are suitable for the construction of estimators or predictors for many model-based adaptive control strategies.…”
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