Search Results - feed-forward ((((extraction algorithm) OR (selection algorithm))) OR (pollination algorithm))
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Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network
Published 2014“…First, the best set of structures for feed forward neural network were found by multi objective parallel genetic algorithm. …”
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Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar
Published 2015“…The SLFN is trained by an algorithm named Extreme Learning Machine (ELM). The extracted features will be fed up into SLFN to classify the fault. …”
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Fault classification in transmission line using single layer feed-forward network trained by extreme learning machine / Muhamad Azfar Abd Ghafar
Published 2015“…The SLFN is trained by an algorithm named Extreme Learning Machine (ELM). The extracted features will be fed up into SLFN to classify the fault. …”
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CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING
Published 2017“…The solution set (i.e. optimized weight/bias matrix of ANN) provided by the optimized and improved genetic algorithm and modified BP based model is extracted and used in the design and development of a prototype device of the proposed model. …”
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PROPOSED METHODOLOGY FOR OPTIMIZING THE TRAINING PARAMETERS OF A MULTILAYER FEED-FORWARD ARTIFICIAL NEURAL NETWORKS USING A GENETIC ALGORITHM
Published 2011“…ANN can be categorized into three main types: single layer, recurrent network and multilayer feed-forward network. In multilayer feed-forward ANN, the actual performance is highly dependent on the selection of architecture and training parameters. …”
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Bacteria identification via Artificial Neural Network based-on Bergey’s manual
Published 2017“…Levenberg Marquardt algorithm based Feed-forward backpropagation with Multilayer perceptron type of ANN was used in the training and learning sessions of the ANN development in order to obtain high accuracy simulation results within short period of time.…”
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A Hybrid Neural Network-Based Improved PSO Algorithm for Gas Turbine Emissions Prediction
Published 2025“…The PSO adopts a unique random number selection strategy, incorporating the K-Nearest Neighbor (KNN) algorithm to reduce prediction errors. …”
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A preliminary study on automated freshwater algae recognition and classification system / Hayat Mansoor Abdullah
Published 2012“…Finally,41of geometrical, texture, and novel features were normalized to feed into artificial neural network (ANN) for classification and recognition purposes. The Feed-forward multilayer perceptron network with back propagation error algorithm (MLP) initialized, and trained with extracted database feature of selected algae image samples. …”
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Removal of heavy metals from water by functionalized carbon nanotubes with deep eutectic solvents: An artificial neural network approach / Seef Saadi Fiyadh
Published 2019“…The best result achieved for Pb2+ removal using ANFIS algorithm is with RE 7.078%. For As3+ removal using different adsorbents, two algorithms were applied for the modelling, the feed-forward back-propagation maximum RE achieved is 5.97% while, the NARX algorithm achieved better accuracy with maximum RE of 5.79%. …”
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Introducing new statistical shape based and texture feature extraction methods in the plant species recognition system
Published 2013“…As the classification result, radial basis neural networks (RBFNN), feed forward neural networks (FFNN), neural networks using genetic algorithm (NNUGA) shows 100%, 93%, 97.3% of accuracy respectively . …”
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Skin Hydration And Transepidermal Water Loss Measurement Using Vis/nir Spectroscopy And Feed-forward Backpropagation Neural Network
Published 2022“…In this study, the prediction models were built using simple linear regression, multiple regression analysis, and feed-forward backpropagation neural network (FFBPNN) with gradient descent algorithm. …”
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Infant cry recognition system: A comparison of system performance based on Mel frequency and linear prediction cepstral coefficients
Published 2023Subjects:Conference paper -
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Automatic recognition of freshwater algae (Oscillatoria sp.) using image processing techniques with artificial neural network approach / Awatef Saad Salem Saad
Published 2012“…Computer-based image analysis and pattern recognition methods were used to construct a system that is able to identify, and classify selected Cyanobacteria genus automatically. An image analysis algorithm was implemented to contrast, filter, isolate and recognize objects from microscope images. …”
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Semantic focus fusion based on deep learning for deblurring effect
Published 2024“…Semantic focus fusion architecture contains focus extraction, feed-forward mapping, and upsampling modules. …”
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Using perceptron feed-forward Artificial Neural Network (ANN) for predicting the thermal conductivity of graphene oxide-Al2O3/water-ethylene glycol hybrid nanofluid
Published 2021“…Then, to generalize the data and obtain a function, the Perceptron feed-forward ANN was used, simulating the output parameter. …”
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Voltage stability margin identification using evolution programming learning algorithm / Zamzuhairi Darus
Published 2003“…This project proposed on an investigation on the voltage stability margin identification using evolution programming learning algorithm. A multilayer feed-forward artificial neural network (ANN) with evolution programming learning algorithm for calculation of voltage stability margins (VSM). …”
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Overhead vision system for mobile robot orientation detection
Published 2011“…The extracted features are then used as the inputs to a simple feed forward neural network. …”
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Data Entry using Handwriting Recognition Techniques
Published 2007“…Distinctive features from each character are extracted using the combination of five feature extraction modules. …”
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Conference or Workshop Item -
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Optimisation of neural network with simultaneous feature selection and network prunning using evolutionary algorithm
Published 2015“…For feed forward network, most of the optimisation are merely on the Weights and the bias selection which is generally known as conventional Neuroevolution. …”
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