Search Results - (( using function method algorithm ) OR ( _ identification learning algorithm ))
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RECURSIVE LEARNING ALGORITHMS ON RBF NETWORKS FOR NONLINEAR SYSTEM IDENTIFICATION
Published 2010“…This thesis proposes derivative free learning, using finite difference, methods for fixed size RBF network in comparison to gradient based learning for the application of system identification. …”
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2
Development of multi-objective load shedding optimization via back tracking search algorithm with novel reactive power tracing index
Published 2023“…Electric power plant loads; Learning algorithms; MATLAB; Multiobjective optimization; Optimization; Reactive power; Back tracking; Backtracking search algorithms; Identification method; Load-shedding; Multi-objective functions; Power flow simulation; System contingencies; Under voltage load shedding; Electric load shedding…”
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A multi-objective genetic type-2 fuzzy extreme learning system for the identification of nonlinear dynamic systems
Published 2017“…However, other than RMSE, the maximum absolute error (MAE) for each of identification samples is very important. This paper propose a novel hybrid learning algorithm for the design of IT2FLS. …”
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4
Identification of continuous-time model of hammerstein system using modified multi-verse optimizer
Published 2021“…In particular, the search capacity of the MVO algorithm has been improved using the sine and cosine functions of the Sine Cosine Algorithm (SCA) that will be able to balance the processes of exploration and exploitation. …”
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5
Adaptive model predictive control based on wavelet network and online sequential extreme learning machine for nonlinear systems
Published 2015“…Moreover, the ability of initialization the hidden nodes parameters using density function and recursive algorithm will help WN-OSELM to perform useful generalization facility and modeling accuracy. …”
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6
Opposition- based simulated kalman filters and their application in system identification
Published 2017“…This technique can be an alternative approach to solve system identification problem, apart from using conventional method.…”
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7
Bio-signal identification using simple growing RBF-network (OLACA)
Published 2007“…An enhanced online adaptive centre allocation algorithms (or resource allocation network (RAN)) using simple/stochastic back-propagation method with minimal weight update variant are developed for direct-link radial basis function (DRBF) networks. …”
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8
Odour based human identification and classification using neural networks
Published 2019“…The unsurpassed framework for algorithm learning to be used for human identification can be back propagation learning algorithm named the Levenberg-Marquardt. …”
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Human odour detection approach using machine learning
Published 2019“…The unsurpassed framework for learning algorithm to be used for human identification is Levenberg-Marquardt backpropagation learning algorithm. …”
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10
A comparative study of vibrational response based impact force localization and quantification using different types of neural networks / Wang Yanru
Published 2018“…It may be ore accurate than MLPwhen there are multiple outputs. In addition, ANFIS uses hybrid learning algorithm. It is mixed with least mean square and gradient descent method, which cause many advantages, such as much better learning ability and less computational time. …”
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11
Real-time identification of an unmanned quadcopter flight dynamics using fully tuned radial basis function network
Published 2018“…Recursive system identification based on neural network (NN) offers an alternative method for quadcopter dynamics modelling. …”
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12
Effective query structuring with ranking using named entity categories for XML retrieval
Published 2016“…Furthermore, it employs Predicates Identification Algorithm (PIA) and Entity Identification Algorithm (EIA) to identify user search intention. …”
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Analysis of banana plant health using machine learning techniques
Published 2024“…Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. …”
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Fault classification in smart distribution network using support vector machine
Published 2023“…Gaussian radial basis function (RBF) kernel function has been used for training of SVM to accomplish the most optimized classifier. …”
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Handgrip strength evaluation using neuro fuzzy approach
Published 2010“…Handgrip assessment is a useful method to monitor patient rehabilitation. The neurofuzzy analysis provides system identification and interpretability of fuzzy models and learning capability of neural networks. …”
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Named entity recognition using a new fuzzy support vector machine.
Published 2008“…Some of the Machine learning algorithms used in NER methods are, support vector machine(SVM), Hidden Markov Model, Maximum Entropy Model (MEM) and Decision Tree. …”
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Operational structural damage identification using de-noised modal feature in machine learning / Chen Shilei
Published 2021“…By integrating ISMA, both supervised and unsupervised machine learning algorithms were investigated to develop real-time damage identification schemes. …”
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Damage identification using experimental modal analysis and adaptive neuro-fuzzy interface system (ANFIS)
Published 2012“…Using a given input-output data set, ANFIS constructs a Fuzzy Inference System (FIS) whose fuzzy membership function parameters are adjusted using combination of back propagation algorithm with a least square type of method. …”
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Personal identification by Keystroke Pattern for login security
Published 2001“…Several other factors such as momentum, learning rate and various weight initialization were used for comparison. Three types of weight initialization were used, including Nguyen-Widrow (NW), Random and Genetic Algorithm (GA). …”
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20
Nonlinear dynamic system identification and control via self-regulating modular neural network
Published 2003“…The proposed network is devised to overcome the redundancy problems of Gaussian neural networks that use square mesh partition method. In the SGMN, the problem space is decomposed into overlapping regions by expertise domain and the local expert models are graded according to their expertise level. …”
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