Prediction of Syllable Duration Using Structure Optimised Cuckoo Search Neural Network (SOCNN) for Text-To-Speech

2016 ◽  
Vol 13 (10) ◽  
pp. 7538-7544
Author(s):  
T Jayasankar ◽  
J. Arputha Vijayaselvi
Keyword(s):  
Neural Network ◽  
Speech Synthesis ◽  
Unit Length ◽  
Back Propagation ◽  
Cuckoo Search ◽  
National Language ◽  
Text To Speech ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
The Neural Network

A Feed Forward Neural Network (FFNN) model primarily based unrestricted delivery prediction of language unit length pattern info speech synthesis system is that the focus of this paper. Estimation of delivery parameter of segmental length plays a essential half in unrestricted concatenative synthesis Text To Speech System (TTS) is capable of synthesize natural sounding speech with improved quality. Common options to coach the Neural Network enclosed language unit position within the phrase, context of language unit, language unit position within the word, language unit nucleus and amp; language unit identity square measure extracted from the text. Back-propagation Neural Network (BPNN) formula is one in every of the foremost wide used and a preferred technique to optimize the feed forward neural network coaching in delivery prediction. For enhance the accuracy of delivery prediction language unit length in neural BP, that’s Cuckoo Search formula to seek out the structure of the neural network with least weights while not compromising on the prediction error is planned. Speech information is adopted to check the length prediction performance of planned SOCNN, wherever the obtained results demonstrate a marked improvement over the essential BP. The system performance is shown mistreatment the synthesizing natural sounding speech for Tamil, national language of Republic of India.

Contributions to Predict the Malfunction Probability of the Human-Machine-Environment System, Using Artificial Neural Networks

2015 ◽  
Vol 760 ◽  
pp. 771-776
Author(s):  
Daniel Constantin Anghel ◽  
Nadia Belu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Experiment ◽  
Manufacturing Processes ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Linear Relationships ◽  
The Neural Network ◽  
Artificial Neural

This paper presents the application of Artificial Neural Networks to predict the malfunction probability of the human-machine-environment system, in order to provide some guidance to designers of manufacturing processes. Artificial Neural Networks excel in gathering difficult non-linear relationships between the inputs and outputs of a system. We used, in this work, a feed forward neural network in order to predict the malfunction probability. The neural network is simulated with Matlab. The design experiment presented in this paper was realized at University of Pitesti, at the Faculty of Mechanics and Technology, Technology and Management Department.

scholarly journals Study of Variants of Extreme Learning Machine (ELM) Brands and its Performance Measure on Classification Algorithm

2021 ◽  
Vol 3 (2) ◽  
pp. 83-95
Keyword(s):  
Neural Network ◽  
Computation Time ◽  
Performance Measure ◽  
Feed Forward Neural Network ◽  
Forward Algorithm ◽  
Feed Forward ◽  
Research Article ◽  
The Neural Network ◽  
Real Essence ◽  
Hidden Nodes

Recently, the feed-forward neural network is functioning with slow computation time and increased gain. The weight vector and biases in the neural network can be tuned based on performing intelligent assignment for simple generalized operation. This drawback of FFNN is solved by using various ELM algorithms based on the applications issues. ELM algorithms have redesigned the existing neural networks with network components such as hidden nodes, weights, and biases. The selection of hidden nodes is randomly determined and leverages good accuracy than conservative methods. The main aim of this research article is to explain variants of ELM advances for different applications. This procedure can be improved and optimized by using the neural network with novel feed-forward algorithm. The nodes will mainly perform due to the above factors, which are tuning for inverse operation. The ELM essence should be incorporated to reach a faster learning speed and less computation time with minimum human intervention. This research article consists of the real essence of ELM and a briefly explained algorithm for classification purpose. This research article provides clear information on the variants of ELM for different classification tasks. Finally, this research article has discussed the future extension of ELM for several applications based on the function approximation.

scholarly journals Temporal solar irradiance variability analysis using neural networks

2015 ◽  
Vol 11 (S320) ◽  
pp. 333-338
Author(s):  
Ambelu Tebabal ◽  
Baylie Damtie ◽  
Melessew Nigussie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Solar Irradiance ◽  
Dominant Role ◽  
Back Propagation ◽  
Total Solar Irradiance ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Surface Magnetism ◽  
Feed Forward

AbstractA feed-forward neural network which can account for nonlinear relationship was used to model total solar irradiance (TSI). A single layer feed-forward neural network with Levenberg-marquardt back-propagation algorithm have been implemented for modeling daily total solar irradiance from daily photometric sunspot index, and core-to-wing ratio of Mg II index data. In order to obtain the optimum neural network for TSI modeling, the root mean square error (RMSE) and mean absolute error (MAE) have been taken into account. The modeled and measured TSI have the correlation coefficient of about R=0.97. The neural networks (NNs) model output indicates that reconstructed TSI from solar proxies (photometric sunspot index and Mg II) can explain 94% of the variance of TSI. This modeled TSI using NNs further strengthens the view that surface magnetism indeed plays a dominant role in modulating solar irradiance.

scholarly journals Effects of Transcranial Magneto-acoustic Electrical Stimulation on Discharge propagation in feed-forward neural network

2022 ◽  
Author(s):  
Jiankang Wu ◽  
Shuai Zhang ◽  
Jiayue Xu ◽  
Junwu Dang ◽  
Qingyang Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Electrical Stimulation ◽  
Magnetic Stimulation ◽  
Inhibitory Neurons ◽  
Discharge Frequency ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Regulation Effect ◽  
The Neural Network ◽  
Better Regulation

Abstract The mammalian brain has an extremely complex, diversified and highly modular structure, and information dissemination in the modular brain network affects various brain diseases. Although a variety of neuromodulation techniques have been used to study the discharge characteristics of neural networks, the effects of transcranial magneto-acoustic electrical stimulation(TMAES) have rarely been mentioned. Based on the excitatory and inhibitory Izhikevich neuron model, we constructs a feed-forward neural network connected by electrical synapses and chemical synapses, and analyzes the firing frequency of the neural network under TMAES and magnetic stimulation and the differences in each layer types of firing patterns of neurons. The results showed that the discharge patterns of neurons in each layer were different, the discharge frequency of inhibitory neurons was higher than that of excited neurons, and the stimulation signal could be transmitted to the whole network layer.The maximum discharge frequency of neural network connected by electrical coupling can reach 0.94kHz, and the discharge frequency of neural network connected by chemical coupling is less than 0.5 kHz.With the increase of coupling degree, the discharge frequency of neurons in each network layer under TMAES is much greater than that under magnetic stimulation.When the induced current is less than 26.5μA/cm 2 , magnetic stimulation can promote the inhibitory neurons, and TMAES has a variety of regulatory effects on the inhibitory neurons in the neural network. The results show that TMAES has better regulation effect than magnetic stimulation, and the regulation effect is affected by neural network structure and stimulation parameters.

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