multi layer perceptron-neural networks
Evaluation of 1 D tracer concentration profile in a small river by means of Multi Layer Perceptron Neural Networks
... using Multi-Layer Perceptron Neural Networks to evaluate the whole concentration versus time profile at several cross-sections of a river under var- ious flow conditions, using as ...
14
A Survey on Applications of Multi Layer Perceptron Neural Networks in DOA Estimation for Smart Antennas
... input layer, 1 neuron in the output layer 16 and 20 neurons in the hidden layers for optimum ...RBF neural networks and the other known as sectorization uses a combination of both MLP and RBF ...
7
Comparison between Cascade Forward and Multi-Layer Perceptron Neural Networks for NARX Functional Electrical Stimulation (FES)-Based Muscle Model
... using Multi-Layer Perceptron and Cascade Forward Neural Network ...optimal Multi-Layer Perceptron (MLP) results were obtained from input lag space of 1, output lag space ...
7
Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm
... Multilayer perceptron models, which are constructed based on nervous system of human brain, has high capabilities in modeling nonlinear behavior of complex ...the neural networks but among all of ...
20
Comparison between Multi Layer Perceptron and Radial Basis Function Networks for Sediment Load Estimation in a Tropical Watershed
... River suspended sediment load is a principal parameter in reservoir management and can serve as an index to understand the status of soil erosion and ecological envi- ronment in a watershed [1]. The rainfall-sediment ...
7
Automated epileptic seizures detection using multi-features and multilayer perceptron neural network
... artificial neural networks classifier-based automated detection of epileptic ...three- layer MLPNN, and a classification rate of more than 95% was ...Back-propagation neural network classifier ...
10
Prediction of the waste stabilization pond performance using linear multiple regression and multi-layer perceptron neural network: a case study of Birjand, Iran
... optimized to minimize computer processing and obtain a good performance to avoid overfitting (10). There is no way to determine the best number of hidden units without training several networks and estimating the ...
9
Earthquake Hazard Safety Assessment of Existing Buildings Using Optimized Multi-Layer Perceptron Neural Network
... a multi-layer perceptron (MLP) with a back-propagation (BP) algorithm by means of a database that was improved over a statistical process named P25 ...to perceptron networks for ...
16
A GENETIC ALGORITHM OPTIMIZED MULTI-LAYER PERCEPTRON FOR SOFTWARE DEFECT PREDICTION
... Most broadly utilized neural network model for classification is MLP in light of one or all the more consecutively joined layers of perceptron. MLP model considered in this paper fits in with the feed ...
10
Hybrid Optimized Back propagation Learning Algorithm for Multi layer Perceptron
... biological neural system artificial neural network model is ...biological neural networks may hold the key to the success of solving intelligent tasks by machines [1] A neural network ...
5
Evolved Topology Generalized Multi-layer Perceptron (GMLP) for Anatomical Joint Constraint Modelling
... The ANNs underlying structure is encoded such that GA techniques can be applied. A Direct Encoding method is used to convert between the phenotype (neural networks) and genotype (its genetic representation) ...
6
Open Source Software Survivability Prediction Using Multi Layer Perceptron
... Each layer in a neural network is made up of several such perceptrons, which take in some input, apply an activation function on the input and output some value based on the ...training neural ...
10
Analysis of Multi layer Perceptron Network
... these neural networks ...hidden layer processing ...common neural network architecture such as multilayer ...multilayer Perceptron is a feedforward artificial neural network ...
7
Data Aggregation Framework for Clustered Sensor Networks Using Multi Layer Perceptron Neural Network
... Sensor network consists of many nodes and they are deployed closely. The positions of the nodes are not pre planned. These sensor nodes are helpful for disaster relief operations. There are many different types of ...
5
Seismic Signal Classification using Multi Layer Perceptron Neural Network
... Seismic waves can be produced by many types of sources. The latter include tectonic, quarry blast, underground nuclear explosions and cultural activities. These seismic waves are detected by seismic monitoring ...
9
Wavelet support vector machine and multi-layer perceptron neural network with continues wavelet transform for fault diagnosis of gearboxes
... various neural networks, it is of interest to set the number of hidden nodes between 5 to 30 for different ...hidden layer of MLP, can give model, which have the best performance in the verification ...
13
Joint Constraint Modelling Using Evolved Topology Generalized Multi-Layer Perceptron(GMLP)
... Before GA techniques can be applied to topological evolution the ANNs underlying structure, the phenotype, must be considered as a genotype (a blue print for the construction of the network.) This must be encoded such ...
12
Efficiency of Multilayer Perceptron Neural Networks Powered by Multi Verse Optimizer
... The architecture of the MLP corresponds to the dataset. The size of input and output layers and the number of dataset attributes are static according to the dataset. While the size of hidden layer is dynamic. The ...
8
Efficient Cancer Clustering and Classification Using Multi Layer Feed Forward Artificial Neural Networks
... Training the MFFANN the input photo is cut up into blocks or vectors of 4x4, 8×8 or 16×16 pixels. Those vectors are used as inputs to the community. The network is offer by the predicted (or the favored) output, and it's ...
9
Consistent community detection in uni-layer and multi-layer networks
... a multi-layer network or multiple snapshots of a time-varying ...of multi-view clustering in the past decade based on the spectral clustering or a low-rank matrix ...in multi-layer ...
243