multi-input neural network
To Improve the Performance of Secondary Structure Prediction by Soft Computing Technique
... as neural network has been widely used in predicting secondary structures of amino acid ...structures. Multi-layered feed forward neural network model is trained with the help of hidden ...
5
Online Full Text
... new neural-network-based decoupled sliding-mode controller scheme to solve the control of a class of multi-input-multi-output (MIMO) nonlinear dynamic systems via the SPSA ...the ...
6
Development of a new EDRNN procedure in control of human arm trajectories
... of neural networks in solving complex problems, their design procedure is based on still trial-and-error ...a neural network is the proper choice of the network architecture, especially used ...
24
ECG SIGNAL PROCESSING USING BPNN & GLOBAL THRESHOLDING METHOD
... tasks. Neural Network is one of the most used methods of ECG beat classification, Multi-Layer Perception (MLP) based on the Neural Networks has been chosen to be able to classify the ECG ...
6
Scaly Neural Networks for Speech Recognition Using DTW and Time Alignment Algorithms
... features. Neural Networks are, in essence, biologically inspired networks since they are based on the current understanding of the biological nervous ...a network of densely interconnected simple processing ...
9
The Effect of Process Parameters on Penetration in Shielded Metal Arc Welding under Magnetic Field using Artificial Neural Networks
... a multi-layer feed forward artificial neural network with back propagation algorithm was modeled to predict the effects of welding input process parameters on weld bead ...
6
Architecture of the Extended-Input Binary Neural Network and Applications
... artificial neural network is inspired by the structure and function of the major parts of the ...an input module that resemble the sensory (stimuli) area and an output module similar to the motor ...
7
Quantum Inspired Neural Network with Sequence Input
... of input information, QNNSI and ANN take different ...the input is circularly mapped to the output of quantum controlled-not gates in hidden ...of multi-qubits, therefore, this map- ping is highly ...
11
Simultaneous optimization of material removal rate and surface roughness for WEDM of WC-Co composite using grey relational analysis along with Taguchi method Pages 479-490 Download PDF
... order multi-variable regression model and a feed forward back-propagation neural network to correlate the input process parameters, such as pulse on time, pulse-off time, peak current and ...
12
Brain Tumor Segmentation from Multi modality MRI Data Based on Tamura Texture
... Abstract. A segmentation algorithm of brain tumor MR image based on Tamura texture feature and BP Neural Network is proposed in this paper. Firstly, the local grayscale features of four modal MR images are ...
6
Comparative study of static and dynamic neural network models for nonlinear time series forecasting
... as neural network models, in modeling and forecasting the market indexes can yield impressive results (Aladag et ...as neural network models, is a response to the lack of consensus on ...
18
Argument Component Classification for Classroom Discussions
... the neural network models to specialize towards claims and evidence, with much weaker performance on war- ...non neural net- work models, although the impact on performance is ...
11
Analysis of Groundwater for Potability from Tiruchirappalli City Using Backpropagation ANN Model and GIS
... artificial neural network was then developed using part of the experimental data for training and ...the neural network model was applied to Collec- tion of available data about the various ...
8
Title: DIABETIC RETINOPATHY DETECTION USING DEEP NEURAL NETWORK
... Preprocessing will help to filter the images in the dataset and avoid noises. This organization of image data helps in training the neural network. Once the data is organized it is ready for preprocessing ...
6
Neural Network Design - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... training neural net- works to perform various ...perceptron network has fundamental limitations, but it is impor- tant for historical reasons and is also a useful tool for introducing key con- cepts that ...
1012
Download Download PDF
... artificial neural network (ANN) application of load frequency control (LFC) of a Multi-Area power system by using a neural network controller is ...artificial neural networks ...
8
Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO
... of input and average of ...different input parameters so this parameter is known as the most effective parameters on acid solubility in supercritical carbon ...
36
Enhancing recurrent neural network-based language models by word tokenization
... different neural network architectures to estimate the language models from a given corpus using unsupervised learning neural net- works ...Generally, neural networks have demonstrated success ...
13
New Distributed Data Fusion Using Pregel For Large Text Dataset
... considers multi-typed data, interconnected datasets including the structured, unstructured and semi- structured dataset which are mostly available in heterogeneous information ...information network model ...
9
Modeling Input Uncertainty in Neural Network Dependency Parsing
... Recently, neural network dependency parsers (Chen and Manning, 2014; Dyer et al., 2015; Kiperwasser and Goldberg, 2016) obtained state-of-the-art performance for dependency parsing. These parsers ...
8