Transfer Function Of Network Research Articles

Real-time gypsum quality estimation in an industrial calciner: A neural network-based approach

Total bound moisture (TBM) is a typical quality indicator of industrial-grade gypsum. This gypsum is comprised of three distinct phases, namely anhydrite, dihydrate, and hemihydrate, of which only the latter is of much industrial use. TBM analysis is a lengthy laboratory procedure, and an artificial neural network (ANN) TBM inference measurement is proposed as a fast and online alternative. An ANN inference model for gypsum TBM based on plant data was developed. The inputs to the network were primarily focused on the plant's calciner, and different network topologies, data divisions, and transfer functions were investigated. Furthermore, the applicability of the TBM value as a quality indicator was investigated based on a gypsum phase analysis. A strong correlation between TBM and the gypsum hemihydrate and anhydrite content was found, validating the plant target TBM of 5.8% as a quality indicator. A network topology consisting of one hidden layer with logarithmic-sigmoid (logsig) and pure linear (purelin) transfer functions showed the best performance (R > 90%).

Machine learning-based modelling using ANN for performance prediction of a solar air heater design with jet impingement

The prediction of heat transfer performance for solar air heater (SAH) designs is important to assess its potential without relying on extensive experimental studies. This work aimed to predict the heat transfer characteristics of a SAH design with jet impingement using an extensive artificial neural network (ANN) study and covering a complete year-long ambient and various operating parameters (commensurate and non-commensurate). Total 84 ANN models were developed using different network types, transfer functions, and training functions. They were tested on 174 representative datasets collected throughout a year. The best ANN model was found to be the feed forward backpropagation model with tangent-sigmoid transfer function and LM training algorithm, which produced accurate predictions for thermal efficiency, exergy efficiency, and thermo-hydraulic efficiency, with sample errors within ±5 %. The numerical values of R2, MAE, RMSE, and COV from the ANN model results were respectively 0.9972, 0.9053, 1.1884, and 2.0618 for thermal efficiency, 0.9947, 0.2631, 0.3363, and 1.7157 for exergy efficiency, and 0.996, 0.0139, 0.0193, and 0.8076 for thermo-hydraulic efficiency. The study concluded that ANN models can accurately predict the heat transfer characteristics of an SAH design, requiring less time and less computational resources compared to conventional experimental or computational methods.

Optimization of Artificial Neural Network Transfer Function for Hydrological Modelling: A Review

Optimization of Artificial Neural Network Transfer Function for Hydrological Modelling: A Review

Optimization and Design of Circuit Parameters of Aircraft Engine Speed Acquisition System

Rotating speed is an important parameter to reflect the running condition of the engine, and its real-time measurement and accuracy are directly related to the control accuracy of the engine. In this paper, according to the actual demand for the measurement of engine speed, a system architecture and a hardware design scheme of frequency acquisition circuits are proposed. The overall simulation is carried out by Saber. According to technical requirements, the transfer function of the band-pass filter network and the hysteresis threshold are analyzed and calculated theoretically. By adjusting the RC value of the low-pass filter and introducing the reference voltage to reduce the hysteresis threshold, the acquisition failure caused by the low output excitation when the engine is running at low speed is solved. The high-precision acquisition in the full speed range is realized, with ideal simulation.

Artificial neural network for predicting the physicochemical composition of milk from physical analyses

The aim of the research was to predict, using Artificial Neural Network (ANN) the physicochemical composition of milk from physical analyses. Forty-three milk samples were analyzed for fat, solids-non-fat, protein, lactose, and mineral salts. In addition to temperature, freezing point, and density by means of an ultrasound milk analyzer. The network architecture used was feed-forward multilayer, with data divided into 70% for training and 30% for ANN testing. The artificial neural network transfer function was Relu, Adam as the training algorithm for weight change with a constant learning rate. The number of neurons in the hidden layer was determined using the mean square error and coefficient of determination for training and test data. Twenty neurons in the hidden layer were analyzed and considered appropriate. The ANN model was able to predict the physicochemical composition of milk, the result obtained was a robust coefficient of determination, with values above 0.88.

Equilibrium Violation From the Complex Stiffness

Abstract Evidence from cyclic tests on metals, elastomers, and sandy soils reveals that damping forces are nearly rate-independent and structural (hysteretic or rate-independent) damping was widely adopted since the 1940s. While there is no time-domain constitutive equation for a linear spring connected in parallel with a rate-independent dashpot, the dynamic stiffness (transfer function) of this mechanical network can be constructed in the frequency domain; and it was known since the early 1960s that this mechanical network exhibits a non-causal response. In view of its simplicity in association with the wide practical need to model rate-independent dissipation, this mechanical network was also implemented in time-domain formulations with the label complex stiffness where the force output, P(t) is related in the time domain to the displacement input, u(t), with P(t) = k(1 + iη)u(t). This paper shows that the complex stiffness, as expressed in the time domain by various scholars, is a fundamentally flawed construct since in addition to causality it violates equilibrium.

Applying regression techniques to determine mathematical equations of exergy, electricity, and energy values of photovoltaic thermal collector

The highlight of this study is to produce mathematical equations that can calculate the electricity, energy, and exergy efficiency values of an air-type photovoltaic-thermal (PV-T) solar collector with the help of a regression-based artificial intelligence method. Contrary to classical predictive artificial intelligence models, the Elastic.Net regression method, which can produce a linear equation for the modeled parameter, was used. Six different data sets were obtained, with the trials carried out in two months for the PV-T efficiency values. The Elastic Net algorithm produced energy, electrical, and exergy efficiency equations with the first, third, and fifth datasets. Comparisons were made between experimental thermal efficiency ratings and the output of efficiency formulas. The other three data sets were then subjected to efficiency equations, and the results were contrasted with the experimental values. The thermal efficiency equations predicted the experimental data in the data sets 1, 3, 5 and, 2, 4, 6 with mean MAPEs of 2.9% and 4.16%, respectively. The experimental values in both group data sets have been said to be comparable to the efficiency values discovered using the equations. As a result, it is anticipated that the efficiency equations developed can be used in various experimental configurations or flat solar collectors. Additionally, prediction models were created utilizing datasets 1, 3, and 5 using the artificial neural network (ANN) technique. The best ANN network structure was obtained using different network types, training algorithms, transfer functions, and layer-neuron numbers in ANN models.

Coupling Vibration Analytical Model and Vibration Suppression of Linear Motor Driven Free Piston Stirling Cryocoolers

Linear motors are widely utilized in free-piston Stirling cryocoolers (FPSCs) due to their high efficiency, high reliability, and low noise. The reciprocating motion of the compression piston and the expansion displacer in a FPSC will inevitably lead to the vibration output. Most researches only focused on the vibration of a single moving component, and few studies discussed about the coupling vibration of linear compressor and expander. In this paper, the vibration of a linear motor-driven Stirling cryocooler is studied through theoretical analysis and experiments. A passive absorber is designed to suppress the vibration output from the linear compressor and expander simultaneously. The dynamic model of the Stirling cryocooler system is established, and the network transfer function between the input voltage and the system displacement of the linear motor is derived. The influences of these linear motor parameters of thrust constant, resistance and inductance, and working condition of charged pressure, is investigated in detail, on the system's vibration output. The experimental results are presented to evaluate the validity of the proposed model.

Prediction of secondary components in sugarcane brandy by application of artificial neural network

This study aimed to use artificial neural networks to predict the secondary components of sugarcane brandy. We got data on the characteristics of sugarcane brandy from the literature. We divided this data into input data and output data. Secondary components in sugarcane brandy were the output data. The architecture used for artificial neural networking was the multilayer feed-forward network, which features a hidden layer. These hidden neurons have the role of intervening between the input and output layers of the network. We separated the data into 70% for training and 30% for tests. The artificial neural network transfer function was Relu, with Adam as the training algorithm for weight change with a constant learning rate. The number of neurons in the hidden layer was determined using the mean square error. Twenty neurons in the hidden layer were analyzed and considered appropriate, since in the artificial neural network with a greater number of neurons, we observed no significant variation in the reduction of error.

Back-propagation neural network modeling for a pulse tube refrigerator with passive displacer

• The experiment is conducted on a pulse tube refrigerator with passive displacer. • The back-propagation neural network is developed to simulate the performance. • The predicted results of the performance agree with the experimental results. • A relative Carnot efficiency of 23.2% is obtained at 140 K in the prediction. The pulse tube refrigerator (PTR) with passive displacer is a potential alternative for high-efficiency PTR. One of the main problems is that massive experiments are expensive and time-consuming. A back-propagation neural network (BPNN) model is proposed to overcome this challenge. The cooling capacity and relative Carnot efficiency of the PTR with passive displacer can be predicted instead of experiments when the charging pressure, operating frequency, input electrical power, and cooling temperature are determined. The optimal BPNN with the least mean square error is obtained by optimizing the network structure, transfer function, and training function. As a result, the coefficient of determination (R 2 ) values for the training and testing data are 0.9995 and 0.9992, respectively. The predicted values are achieved within eight milliseconds, agreeing with the experimental values. The maximum relative Carnot efficiency of 23.2% occurs at 140 K with a cooling capacity of 15 W. It is concluded that the BPNN is an effective model for simulating the performance of the PTR with passive displacer quickly. Furthermore, the mathematical formulas and the capability to predict the effects of the operating parameters are demonstrated, which is scarce in the literature on the PTR. The BPNN model can be applied to accelerate the development of the PTR with passive displacer.

Frequency Response and Transfer Functions of Large Self-Similar Networks

Abstract Large-scale dynamical systems, no matter whether possessing interconnected appearances, are frequently modeled as networks. For instance, graphs, multi-agent systems, and materials' intricate behaviors are often treated as networked dynamical systems. However, only a few studies have approached the problem in the frequency domain, mostly due to the complexity of evaluating their frequency response. That gap is filled by this paper, which proposes algorithms computing a general class of self-similar networks' frequency response and transfer functions, no matter they are finite or infinite, damaged or undamaged. In addition, this paper shows that for infinite self-similar networks, even when they are damaged, fractional-order and irrational dynamics naturally come into sight. Most importantly, this paper illustrates that for a network under different operating conditions, its frequency response would form a set of neighboring plants, which sets the basis of applying robust control methods to dynamic networks.

Application of machine learning in colloids transport in porous media studies: Lattice Boltzmann simulation results as training data

Colloid transport through a porous medium changes geometrical and hydraulic properties of the pore space. The impact of this effect depends on the colloid types and pore space surface properties which determine the likelihood of pore clogging. Colloid attachment and subsequent detachment are key factors in pore clogging. In this study, the impact of four major fluid and colloids properties on the pore surface and hydraulic conductivity alteration during colloids transport were evaluated using machine learning. These four parameters include solution ionic strength, zeta potential, colloid size and fluid flow velocity.A combined lattice Boltzmann-smoothed profile method was used to simulate accurately coupled mechanisms governing colloid transport to evaluate the impact of the four parameters on the resulting pore space properties after colloid transport. The result of several simulations revealed significant changes of pore surface coverage by the attached colloids, and conductivity, void fraction and coordination number of colloid agglomerates created during transport of individual colloids. Since the simulation of the impact of combination of all possible sets of four parameters is very time consuming, an Artificial Neural Network (ANN) was used as a prognostic method to use the results of several simulations to predict the behavior for a wide range of pore, colloidal and fluid properties. Reported results from a set of 162 simulation case studies for different possible combination of solution ionic strength, zeta potentials, colloid size and flow velocity were selected as input parameters for the machine learning. Four output parameters, namely, pore surface coverage, conductivity, void fraction and coordination number of the colloidal particles were selected.To lower the prediction error value, which is targeted to be lower than 10%, networks were trained 50 times using a MATLAB code, and in each training, after at most 10 epochs, networks were trained. A maximum relative error value of 8.95% was obtained, which is very well within the range of training quality criteria. The results show that the ANN can profoundly predict the simulation outcomes for a wide range of ionic strength (IS) and can be directly used to obtain the value of dependent variables through simple calculations using network weights and transfer functions.

Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust Design

Response surface methodology (RSM) has been widely recognized as an essential estimation tool in many robust design studies investigating the second-order polynomial functional relationship between the responses of interest and their associated input variables. However, there is scope for improvement in the flexibility of estimation models and the accuracy of their results. Although many NN-based estimations and optimization approaches have been reported in the literature, a closed functional form is not readily available. To address this limitation, a maximum-likelihood estimation approach for an NN-based response function estimation (NRFE) is used to obtain the functional forms of the process mean and standard deviation. While the estimation results of most existing NN-based approaches depend primarily on their transfer functions, this approach often requires a screening procedure for various transfer functions. In this study, the proposed NRFE identifies a new screening procedure to obtain the best transfer function in an NN structure using a desirability function family while determining its associated weight parameters. A statistical simulation was performed to evaluate the efficiency of the proposed NRFE method. In this particular simulation, the proposed NRFE method provided significantly better results than conventional RSM. Finally, a numerical example is used for validating the proposed method.

Synergy evaluation model of container multimodal transport based on BP neural network

With the rapid development of economic globalization, the trade of various countries has become increasingly close and thus the rapid growth of container transportation business. The rational choice of transportation routes and intermodal transportation methods has become a hot topic in this field. In order to improve the efficiency of intermodal transportation and reduce the cost of intermodal transportation, this paper studies the evaluation of the synergy effect of container multimodal transportation based on the BP neural network algorithm. First, it is determined that the multi-attribute decision-making method is used to comprehensively evaluate the synergy effect of container multimodal transportation, and the data are normalized. Select the appropriate hidden layer nodes, use Matlab to determine the learning rate, select the logsig function for the network transfer function, select Traingdx as the training function, and establish a container multimodal transport synergy evaluation model based on BP neural network. Secondly, the model is solved, and the problem is transformed into finding the shortest route from Q to P without exceeding the cost and time constraints. Then carry on the simulation experiment, use Matlab to solve the transportation time and total cost between cities. Experimental data show that when the number of iterations is 800, the algorithm begins to converge; at the fifth training, the error between the actual output and the expected output is only 0.0004; in route B, the time of multimodal transportation is 48.18% less than that of single transportation, and the cost is saved by 50.02%. This shows that the container multimodal transport synergy evaluation model based on BP neural network can accurately evaluate the effects, and container multimodal transport can indeed improve transportation efficiency and save costs.

Channel Modeling and Optimization of Leaky Coaxial Cable Network in Coal Mine Based on State Transition Method and Particle Swarm Optimization Algorithm

As a continuous antenna, the leaky coaxial cable (LCX) is widely used in wireless communication of closed or semi-closed areas such as the subway, mine, tunnel, etc. Usually, LCX networks are required in coal mines. Its channel characteristics are of great significance to mobile communication and mine safety. This paper presents an effective channel modeling and optimization method that combines the state transition and the particle swarm optimization (PSO) algorithms for the LCX network. First, a channel model can be established with a few parameters based on the state transition method, which can describe the complex transfer function of the LCX network; then, taking the transfer characteristics of the LCX network as the optimization objective, which should be consistent with the measured results. And the established objective function is optimized and designed with the PSO algorithm. Finally, the proposed method is validated by comparing the measured results and the model-based simulations on two test LCX networks. The experimental results show that the absolute error between the proposed model and the measurement model is less than that of other models at any frequency point. The proposed model can accurately obtain all responses between point-to-point channels at one time. It will help to accurately obtain the performance of different LCX networks and help to detect and locate faults in future studies.

Retracted] Dance Movement Recognition Based on Feature Expression and Attribute Mining

There are complex posture changes in dance movements, which lead to the low accuracy of dance movement recognition. And none of the current motion recognition uses the dancer’s attributes. The attribute feature of dancer is the important high‐level semantic information in the action recognition. Therefore, a dance movement recognition algorithm based on feature expression and attribute mining is designed to learn the complicated and changeable dancer movements. Firstly, the original image information is compressed by the time‐domain fusion module, and the information of action and attitude can be expressed completely. Then, a two‐way feature extraction network is designed, which extracts the details of the actions along the way and takes the sequence image as the input of the network. Then, in order to enhance the expression ability of attribute features, a multibranch spatial channel attention integration module (MBSC) based on an attention mechanism is designed to extract the features of each attribute. Finally, using the semantic inference and information transfer function of the graph convolution network, the relationship between attribute features and dancer features can be mined and deduced, and more expressive action features can be obtained; thus, high‐performance dance motion recognition is realized. The test and analysis results on the data set show that the algorithm can recognize the dance movement and improve the accuracy of the dance movement recognition effectively, thus realizing the movement correction function of the dancer.

Combined Forecasting Method of Ammunition Consumption Based on Wavelet Network

In order to solve the problem that the result of a single algorithm for ammunition consumption prediction is not accurate enough, a combination forecasting method of ammunition consumption based on a wavelet network is proposed. This paper combines three classical methods, namely, task volume estimation method, battle case estimation method and ammunition consumption standard estimation method, which are most commonly used in ammunition consumption prediction, and takes the prediction result of a single method as the input of neural network. By replacing the transfer function of traditional neural network with wavelet basis function, a combined forecasting model of ammunition consumption based on wavelet network is established. The calculation of the example shows that the fitting speed of this method is fast and the accuracy of the result is higher.

The effects of multiple layers feed-forward neural network transfer function in digital based Ethiopian soil classification and moisture prediction

In the area of machine learning performance analysis is the major task in order to get a better performance both in training and testing model. In addition, performance analysis of machine learning techniques helps to identify how the machine is performing on the given input and also to find any improvements needed to make on the learning model. Feed-forward neural network (FFNN) has different area of applications, but the epoch convergences of the network differs from the usage of transfer function. In this study, to build the model for classification and moisture prediction of soil, rectified linear units (ReLU), Sigmoid, hyperbolic tangent (Tanh) and Gaussian transfer function of feed-forward neural network had been analyzed to identify an appropriate transfer function. Color, texture, shape and brisk local feature descriptor are used as a feature vector of FFNN in the input layer and 4 hidden layers were considered in this study. In each hidden layer 26 neurons are used. From the experiment, Gaussian transfer function outperforms than ReLU, sigmoid and tanh transfer function. But the convergence rate of Gaussian transfer function took more epoch than ReLU, Sigmoid and tanh.

Deterministic Multipath Model for the Power-Line Channel

A simple approach of using the multipath model for a power-line channel is proposed. A complex network is decomposed into several point-to-point components, and the transfer function for each individual component is computed. The product of these individual transfer functions gives the transfer function of the whole complex network. Accuracy of the proposed approach is established mathematically and by comparing the simulation results with the traditional methodology.

Pd Dopant for SnO2 Based Thick Film Gas Sensor for Detection of LPG using ANN Technique

In the present paper, studies that it has been made to investigate the sensitivity, response of SnO2 based different Pd-doped thick gas sensor for LPG detection using different non-linear ANN technique at temperature of 350° C. Thick film SnO2 sensor is fictitious on a 1” x 1” alumina substrate. It consists of a SnO2 doped hypersensitive gas layer including different Pd, a pair of electrodes bottom the gas sensing layer serving as a contact pad for sensor. In conjunction with a heater, element designed on the backside of the substrate was printed. The sensitivity of sensor has been studied at different Pd-doped concentration upon exposure LPG for finding the maximum sensitivity at a temperature at 350 c. on the pair of pure and pd doped specimen analysis of conductance and capacitance have been invented by fixed temperature at fixed gas concentration .the sensor sensitivity has been studied at different pd doped at constant temperature of 350° C upon exposure to LPG. The sensitivity and selectivity measurement of sensing ownership are carried out in the range 0- 5000 ppm at 350° C. The maximum response of 72.08% at 350° C was obtained under the explore of 0.5% of LPG. Practical sensitivity of LPG is used to measure the sensitivity of different Pd-doped SnO2 based thick film gas sensors by using ANN algorithm. The performance of ANN models with different non- linear algorithm is evaluated on practical sensitivity of sensor with different network transfer functions. Experimentally, we found that ANN model training algorithm is more suitable for the sensor and sensitivity. ANN as an effective tool in the area of SnO2 based thick film sensor design to find the sensitivity of the LPG gas.