Characterisation of Composite Material Properties by an Inverse Technique

2007 ◽  
Vol 345-346 ◽  
pp. 1319-1322 ◽  
Author(s):  
Evgeny Barkanov ◽  
Andris Chate ◽  
Sandris Ručevskis ◽  
Eduards Skukis
Keyword(s):  
Material Properties ◽  
Core Material ◽  
Inverse Technique ◽  
Error Functional ◽  
Viscoelastic Core ◽  
Response Surface Technique ◽  
The Error Functional ◽  
Vibration Tests ◽  
Planning Of Experiments ◽  
Surface Technique

An inverse technique based on vibration tests to characterise isotropic, orthotropic and viscoelastic material properties of advanced composites is developed. An optimisation using the planning of experiments and response surface technique to minimise the error functional is applied to decrease considerably computational expenses. The inverse technique developed is tested on aluminium plates and applied to characterise orthotropic material properties of laminated composites and viscoelastic core material properties of sandwich composites.

Characterization of Woven Composite Material Properties by Using an Inverse Technique Based on Vibration Tests

2021 ◽  
Vol 903 ◽  
pp. 113-118
Author(s):  
Endija Namsone ◽  
Denis Ermakov
Keyword(s):  
Material Properties ◽  
Woven Composites ◽  
Woven Composite ◽  
Identification Procedure ◽  
Resonant Frequencies ◽  
Inverse Technique ◽  
Physical Experiments ◽  
Vibration Tests ◽  
Non Destructive

A mixed numerical-experimental technique based on vibration tests is modified and applied to determine the elastic material properties of woven composites. This non-destructive technique consists of physical experiments, numerical modelling and material identification procedure. For the purpose of characterization, two carbon fiber panels were prepared by manual layout technology. An evaluation of the accuracy of woven composite elastic properties is executed comparing the numerical and experimentally obtained resonant frequencies.

Improving the Generalization Properties of Radial Basis Function Neural Networks

1991 ◽  
Vol 3 (4) ◽  
pp. 579-588 ◽  
Author(s):  
Chris Bishop
Keyword(s):  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Learning Algorithm ◽  
Error Functional ◽  
Radial Basis ◽  
The Error Functional ◽  
Network Mapping ◽  
Nonlinear Mappings

An important feature of radial basis function neural networks is the existence of a fast, linear learning algorithm in a network capable of representing complex nonlinear mappings. Satisfactory generalization in these networks requires that the network mapping be sufficiently smooth. We show that a modification to the error functional allows smoothing to be introduced explicitly without significantly affecting the speed of training. A simple example is used to demonstrate the resulting improvement in the generalization properties of the network.

scholarly journals Kinetic Modelling and Optimizing of Butyl Propionate over a Synthesied Material (Tungstan Phosphoric Acid) Heteropoly Catalyst Using Response Surface Technique

2021 ◽  
Vol 45 (4) ◽  
pp. 273-280
Author(s):  
Raju Kalakuntala ◽  
Srinath Surnani
Keyword(s):  
Experimental Data ◽  
Phosphoric Acid ◽  
Response Surface ◽  
Kinetic Modelling ◽  
Quadratic Model ◽  
Molar Ratio ◽  
Catalyst Loading ◽  
Response Surface Technique ◽  
Optimized Conditions ◽  
Surface Technique

The performance of heteropoly acid i.e., Tungstan phosphoric acid for the synthesis of butyl propionate at optimized conditions. Effect on conversion and yield of propionic acids using the Response Surface Methodology (RSM) were evaluated by different process parameters including catalyst loading, alcohol/acid molar ratio. There were no external and internal mass transmission limits. A quadratic model acquired by the variance study (ANOVA) has been shown to view experimental data successfully with the regression (R2 = 0.94 and R2 = 0.942) coefficients approaching to unity. The pseudo homogeneous kinetic model (PH) validated with experimental data to determine kinetic parameters i.e., activation energy (45.97 kJ/mol) and frequent factor (91319 L/mol-min).

scholarly journals Learning Precise Spike Train–to–Spike Train Transformations in Multilayer Feedforward Neuronal Networks

2016 ◽  
Vol 28 (5) ◽  
pp. 826-848 ◽  
Author(s):  
Arunava Banerjee
Keyword(s):  
Spike Train ◽  
Gradient Descent ◽  
Probabilistic Models ◽  
Spike Timing ◽  
Inhibitory Neurons ◽  
Closed Form Solutions ◽  
Learning Framework ◽  
Error Functional ◽  
The Error Functional ◽  
Quantities Of Interest

We derive a synaptic weight update rule for learning temporally precise spike train–to–spike train transformations in multilayer feedforward networks of spiking neurons. The framework, aimed at seamlessly generalizing error backpropagation to the deterministic spiking neuron setting, is based strictly on spike timing and avoids invoking concepts pertaining to spike rates or probabilistic models of spiking. The derivation is founded on two innovations. First, an error functional is proposed that compares the spike train emitted by the output neuron of the network to the desired spike train by way of their putative impact on a virtual postsynaptic neuron. This formulation sidesteps the need for spike alignment and leads to closed-form solutions for all quantities of interest. Second, virtual assignment of weights to spikes rather than synapses enables a perturbation analysis of individual spike times and synaptic weights of the output, as well as all intermediate neurons in the network, which yields the gradients of the error functional with respect to the said entities. Learning proceeds via a gradient descent mechanism that leverages these quantities. Simulation experiments demonstrate the efficacy of the proposed learning framework. The experiments also highlight asymmetries between synapses on excitatory and inhibitory neurons.

scholarly journals TOP EVALUATION FOR THE RA TION FOR THE RATE OF FUNC TE OF FUNCTIONAL OF ERROR AL OF ERROR WEIGHT CUBATURE FORMUL TURE FORMULA IN SP A IN SPACE

2019 ◽  
Vol 3 (4) ◽  
pp. 32-37
Author(s):  
Ozodjon Isomidinovich Jalolov ◽  
◽  
Khurshidzhon Usmanovich Khayatov
Keyword(s):  
Sobolev Space ◽  
Upper Bound ◽  
Cubature Formula ◽  
Integration Formulas ◽  
Error Functional ◽  
The Error Functional ◽  
Error Weight ◽  
Norm Of The Error ◽  
Approximate Integration

An upper bound is obtained for the norm of the error functional of the weight cubature formula in the Sobolev space . The modern formulation of the problem of optimization of approximate integration formulas is to minimize the norm of the error functional of the formula on the selected normalized spaces. In these works, the problem of optimality with respect to some definite space is investigated. Most of the problems are considered in the Sobolev space

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