trained artificial neural network
Artificial Neural Network Model for Compressive Strength of Lateritic Blocks
... best trained network was selected. This is the network with the least mean square ...best trained Artificial Neural Network model data for 28th day compressive strength of ...
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System Design of a Computer-Based Clinical Decision Support System Management by Using Radial Basis Function Approach
... a Trained artificial neural network to respond correctly to Input not used during the training ...training. Network generalize ability is related to the concepts of under fitting For ...
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Analysis of cardiovascular (cvd)/coronary heart diseases(chd) using artificial neural network (ann)
... ANN configuration and Back-Propagation Algorithm (Levenberg –Marquardt (LM) algorithm): In the present study a feed forward back propagation ANN is configured for the classification and recognition of ...
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A simple segmentation approach for unconstrained cursive handwritten words in conjunction with neural network
... an Artificial Neural Network is trained with significant amount of valid segmentation points for cursive words ...manually. Trained neural network extracts incorrect ...
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Auditory representations of speech sounds in a neural model: The role of peripheral processing
... trainable artificial neural network ‘back-end’, modeling more central ...suitably trained on synthetic stimuli, the two-stage system is able to reproduce the important effects of category for- ...
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Analytical Fracture Models for Overall Hydraulic Fracturing Reservoirs
... single artificial fracture and its geological model is ...establish artificial neural network. Based on the trained neural network to predict the half-length of ...
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ANN prediction of corrosion behaviour of uncoated and biopolymers coated cp Titanium substrates
... characterization. Artificial Neural Network (ANN) was developed to predict the Open Circuit Potential (OCP) values and Nyquist plot for bare and biopolymer coated cp-Titanium ...the network ...
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Title: A Novel Method for Color Image Recognition
... for artificial neural network(ANN) for recognition purposed, ANN will be trained using the created data base, then will be used after on to recognize a selected color ...
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A machine learning driven sky model
... layer Artificial Neural Network. The network is trained using a sparse set of samples which capture the properties of the lighting at various sun ...
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Artificial neural network trained to predict
... data obtained from the complete 3D simulations, “+” symbols are the results of the ANN predictions. at the cell end. Colors of the symbols dots and “+” are the same for the same energy..[r] ...
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Artificial Neural Network: A tool for Rapid Quantitative Elemental Analysis Using Neutron Activation Analysis
... The main purpose of this work was to explore the applicationof ANN for a fast analysis of NAA in astudy of elemental concentration of some cement samples. It has been provedthat this methodology provides results as good ...
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A. Process of Estimating Position and Velocity with LRF
... with artificial intelligence, ...an artificial intelligence algorithm of multi-layered neural network was designed and trained by deep learning method with many data sets of information ...
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Multi Lingual Speaker Identification on Foreign Languages using Artificial Neural Network
... Next step is extracting the features from these speech signals with the help of MATLAB (A computational language). Five types of features are extracted – LPC, LPCC, Cepstrum coefficient, , average PSD and LSF. Out of ...
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Artificial Intelligence in Diagnosing Tuberculosis: A Review
... the neural network, which are divided into five categories. The neural network is trained with a back-propagation algorithm with a sigmoid ...
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Informative Feature Trained Classification System For Credit Card Fraud Detection
... Neural network based fraud detection methods are most ...of artificial neurons is contained in artificial neural ...of neural network. In neural network ...
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EEG Signal Classification into Seizure and Non-Seizure Class using Discrete Wavelet Transform and Artificial Neural Network
... Figure 1, illustrates proposed method for Seizure, non-seizure detection of EEG signal. system comprises of two stages- training and testing. In training phase input signal is divided in regular time slot and undergoes ...
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Detection and Classification of Leukaemia using Artificial Neural Network
... a Artificial Neural Network (ANN) is designed based on the features ...This neural network is trained to classify the images into their respective ...
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A COMPARATIVE ANALYSIS OF THE RESULTS OF CONVENTIONAL AND COMBINED METHODS OF TRAINING DIRECT PROPAGATION NEURAL NETWORK IN HEALTHY PERSONS TO DETECTING THE DEGREE OF ACTIVITY OF AN AUTONOMOUS NERVOUS SYSTEMFelix A. Pyatakovich*, Lubov V. Khlivnenko, Tatyana I. Yakunchenko, Kristina F. Makkonen, Olga V. MevshaDOWNLOAD/VIEW
... an artificial neural network trained with the help of back error propagation, and of that trained by combining a back error propagation algorithm and a variant of Cauchy stochastic ...
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Framework for a Genetic-Neuro-Fuzzy Inferential System for Diagnosis of Diabetes Mellitus
... After fuzzified, their values were send to FNN and the result is defuzzified. Then obtained results of ANN1 and FNN are used in the second ANN (ANN2) to compute the ultimate output. If the obtained output value is ...
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Application of Artificial Intelligence for Epilepsy Disease
... Deep learning is a precise structure of the group of machine learning methods. Deep learning is a precise structure of depiction- based learning, where a system assimilates and forms fundamental aspects from each ...
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