On using an artificial feedforward neural network as a controller in large-scale power systems

This paper presents a method for incorporating a priori information about an uncertain nonlinear system into the structure of a multilayer feedforward artificial neural network. Known information is incorporated into the activation function of the network output layer. An algorithm is derived for backpropagating the error and updating adjustable parameters within this layer that is consistent with existing supervised learning techniques. The developed technique is applied to the identification of a dynamic system and compared with conventional feedforward artificial neural network identifier. Results exhibit an improvement in the quality of the identification model and an increase in the rate of convergence. As a practical application, a prior information is utilized for identification of switched reluctance motor characteristics on the basis of experimental measurements. The results further demonstrate that artificial neural networks employing a priori information converge faster, require fewer adjustable weights, and more accurately predict the system of interest.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

MicroRNAs (miRNAs) have been found in diverse organisms and play critical role in gene expression regulations of many essential cellular processes. Discovery of miRNAs and identification of their target genes are fundamental to the study of such regulatory circuits. To distinguish the real pre-miRNA from other stem loop hairpins with similar stem loop (pseudo pre-miRNA) is an important task in molecular biology. From the analysis of experimentally proved pre-miRNAs, we identified 17 parameters for miRNA formation. These parameters are grouped into two categories: structural and thermodynamic properties of the pre-miRNAs. A set of feature vector was formed from the pre-miRNA-like hairpins of human, mouse and rat. A feed forward multi layer perceptron Artificial Neural Network (ANN) classifier is trained by these feature vectors. This classifier is an application program, that decide whether a given sequence is a pre-miRNA like hairpin sequence or not. If the sequence is a pre-miRNA like hairpin, then the ANN classifier will predict whether it is a real pre-miRNA or a pseudo premiRNA. The approach can classify correctly the precursors of Human Mouse and Rat, with an average sensitivity of 97.40% and specificity of 95.85%. When compared with previous approaches, MiPred, mR-abela, ProMiR and Triplet SVM classifier, current approach was greater in total accuracy.

Gray layer technology represents a novel method for incorporating prior information about an uncertain nonlinear system into the structure of a multilayer feedforward artificial neural network (ANN). A prescriptive technique is developed that sets or constrains the weights of a particular layer, designated the gray layer, according to a known or partially known function that is related in some manner to the system of interest. An algorithm is derived for backpropagating the error through the gray layer and adjusting the parameters of the gray layer that is consistent with other ANN learning techniques. Gray layer technology is applied to the identification of two different partially known dynamic systems. Results demonstrate a significant improvement in the quality of the identification model and an increase in the rate of convergence.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Headwater streams (HSs) are generally naturally prone to flow intermittence. These intermittent rivers and ephemeral streams have recently seen a marked increase in interest, especially to assess the impact of drying on aquatic ecosystems. The two objectives of this work are (a) to identify the main drivers of flow intermittence dynamics in HS and (b) to reconstruct local daily drying dynamics. Discrete flow states—“flowing” versus “drying”—are modelled as functions of covariates that include information on climate, hydrology, groundwater, and basin descriptors. Three classifiers to estimate flow states using covariates are tested on four contrasted regions in France: (a) a linear classifier with regularization (LASSO for least absolute shrinkage and selection operator) and two non‐linear non‐parametric classifiers, (b) a one‐hidden‐layer feedforward artificial neural network (ANN) classifier, and (c) a random forest (RF) classifier. The three classifiers are compared with a benchmark classifier (BC) that simply estimates dominant flow state for each month based on observations (without using covariates). The performance assessment over the period 2012–2016 carried out by cross‐validation shows that the three classifiers for flow state based on covariates outperformed the BC. This demonstrates the predictive power of the covariates. ANN is the classifier that globally achieves the best performance to predict the daily drying dynamics whereas both RF and LASSO tend to underestimate the proportion of drying states. The covariates are ranked in terms of relevance for each classifier. The monthly proportion of drying states provided by the discrete observation network has a major importance for the three classifiers ANN, LASSO, and RF. This may reflect the proclivity of a site to flow intermittence. ANN gives higher importance to climatic and hydrological covariates and its non‐linearity allows a greater degree of freedom.

In this study, an E-Nose system was realized for the anesthetic dose level prediction. For this purpose, sevoflurane anesthetic agent was measured using the E-Nose system implemented with sensor array of quartz crystal microbalances (QCM). In surgeries, anesthetic agents are given to the patients with carrier gases of oxygen (02) and nitrous oxide (N20). Frequency changes on QCM sensors to the eight sevoflurane anesthetic dose levels were recorded via RS-232 serial port. A multilayer feed forward artificial neural network (MLNN) structure was used to provide the relationship between the frequency change and the anesthetic dose level. The MLNNs were trained with the measured data using Levenberg-Marquardt algorithm. Then, the trained MLNNs were tested with random data. The results have showed that, acceptable anesthetic dose level predictions have been obtained successfully.

Ultrasound (US) imaging is the initial phase in the preliminary diagnosis for the treatment of kidney diseases, particularly to estimate kidney size, shape and position, to give information about kidney function, and to help in diagnosis of abnormalities like cysts, stones, junctional parenchyma and tumors which is shown in Figs. 7–9. This study proposes Grey Level Co-occurrence Matrix (GLCM)-based Probabilistic Principal Component Analysis (PPCA) and Artificial Neural Network (ANN) method for the classification of kidney images. Grey Wolf Optimization (GWO) is used to update the current positions of abnormal kidney images in the discrete searching space, thus getting the optimal feature subset for better classification purposes based on Feed Forward Neural Network (FFNN). The scanned image is pre-processed and the required features are extracted by GLCM, among those, some features are selected by PPCA. Feed Forward Back propagation Neural Network (FFBN) is used to classify the normalities and abnormalities in the part of kidney images. The proposed methodology is implemented in MATLAB platform and the analyzed result produces 98% accuracy using GWO-FFBN technique.

The increase of the world population, especially in the global competition, together with the increasing use of fossil fuel resources to meet energy needs, leads to more greenhouse gases (more than one CO2, methane etc.) emissions and the global climate crisis. In this process, changes in meteorological events such as temperature, precipitation, and wind, attract attention moreover but when considered as a whole, we know that these negative changes in the ecosystem negatively affect many living groups. Sea Surface Temperature (SST) as measured meteorologically is the most important environmental parameter where these changes are monitored and observed. It draws attention to the fact that changes in SST are not limited to living organisms as habitats, but also catalyze many chain reactions, especially socio-economic impacts. Therefore, much of the work is devoted to forecasting studies to adapt to changing habitats and take the necessary precautions against potential risks. Feed-forward artificial neural networks have been commonly used to address these emerging needs. Artificial neural networks, which are a simple imitation of the human neurological system, have been used as an artificial intelligence method in forecasting problems due to their superior performance and not having the limitations of classical time series. In this study, the forecasting of the time series of monthly mean SST temperature obtained from Rize station between the years 2010 and 2020 is performed by using feed-forward artificial neural networks, and the forecasting performance of the corresponding time series is compared with many forecasting methods with different characteristics. The comparison of the methods used the mean square error and mean absolute percentage error criteria, which are commonly used in the forecasting literature. The analysis results showed that the analysis results obtained with the feed-forward artificial neural networks have the best prediction performance. As a result, it can be stated that the sea surface temperature can be forecasted with a very high accuracy using the feed-forward artificial neural networks.

Biogas production estimation using data-driven approaches for cold region municipal wastewater anaerobic digestion

A feedforward artificial neural network has been realized and trained to individualize subharmonics frequencies in an electric network. The adopted sampling window is fixed in 20 ms correspondent to a period of the fundamental frequency of 50 Hz, nevertheless the net is able to identify the subharmonics frequencies even if their period is not completed. The particular binary successive approximation structure realized for the ANN forecasts the scalability of the same net so allowing to recognize the subharmonics with a prefixed resolution. The net has been widely tested on synthesized signals.

&lt;span lang="EN-US"&gt;Image encryption is an important issue in protecting the content of images and in the area of information security. This article proposes a novel method for image encryption and decryption using the structure of the artificial neural network (ANN)-based chua chaotic system (CCS). This structure was efficiently designed on a field-programmable gate array (FPGA) chip utilizing the xilinx system generator (XSG) tool with the IEEE-754-1985 32-bit floating-point number format. For ANN-based CCS design, a multilayer feed forward neural network (FFNN) structure with three inputs and three outputs was created. This structure consists of one hidden layer with four neurons, each of which has a Tangent Sigmoid activation function. The training of ANN-based CCS yielded a 3.602e-13 mean square error (MSE) value. After successfully training the ANN-based CCS, the design was carried out on FPGA, utilizing the ANN structure's bias and weight values as a reference. The xilinx vivado (2017.4) design suite was used to synthesis and test the ANN-based CCS on the FPGA. The histogram, correlation coefficient, and entropy are used to perform security analysis on various images. Finally, FPGA hardware co-simulation using a Xilinx Artix7 xc7a100t-1csg324 chip was utilized to verify that the encryption and decryption of the images were successful.&lt;/span&gt;

Looking at the artificial neural networks’ literature, most of the studies started with feedforward artificial neural networks and the training of many feedforward artificial neural networks models are performed with derivative-based algorithms such as levenberg–marquardt and back-propagation learning algorithms in the first studies. In recent years, although many new heuristic algorithms have been proposed for different aims these heuristic algorithms are also frequently used in the training process of many different artificial neural network models. Pi-sigma artificial neural networks have different importance than many artificial neural network models with its higher-order network structure and superior forecasting performance. In this study, the training of Pi-Sigma artificial neural networks is performed by differential evolution algorithm uses DE/rand/1 mutation strategy. The performance of the proposed method is evaluated by two data sets and seen that the proposed method has a very effective performance compared with many artificial neural network models.

&lt;p&gt;This paper presents an inverse dynamic model estimation based on an artificial neural network of a complete new parallel robot manipulator prototype 6- PGK with six degrees of freedom, built at Petru Maior University of Tirgu-Mures. The model estimation of the parallel robot manipulator is performed with a feedforward artificial neural network. In the control engineering domain there are control structures that need the direct or inverse model of the process for ensuring the process control at the imposed performances. Usually, the determination of the direct/inverse mathematical model is a difficult or impossible task to be achieved. In these cases different non-parametric or parametric, off-line or on-line identification methods are used. A solution that may support the on-line parametric methods is represented by the feedforward artificial neural networks. By implementing feedforward artificial neural networks as a nonlinear autoregressive model with exogenous inputs, the authors investigate the possibility of choosing the optimum parameters that characterize the neural network so that it approximates as better as possible the model of the 6-PGK prototype robot. Finally an innovative algorithm is developed for obtaining the optimal configuration parameters set of the feedforward artificial neural network. The proposed algorithm helps in setting the optimal parameters of the neural network that offer high opportunities to provide satisfactory identification of the robot model. Experimental results obtained by a structure derived from the proposed solution demonstrate a good approximation related to the studied system, which is characterized by nonlinearities and high complexity.&lt;/p&gt;

Training set parallelism and network based parallelism are two popular paradigms for parallelizing a feedforward (artificial) neural network. Training set parallelism is particularly suited to feedforward neural networks with backpropagation learning where the size of the training set is large in relation to the size of the network. This paper analyzes training set parallelism for feedforward neural networks when implemented on a transputer array configured in a pipelined ring topology. Theoretical expressions for the time per epoch (iteration) and optimal size of a processor network are derived when the training set is equally distributed among the processing nodes. These show that the speed up is a function of the number of patterns per processor, communication overhead per epoch and the total number of processors in the topology. Further analysis of how to optimally distribute the training set on a given processor network when the number of patterns in the training set is not an integer multiple of the number of processors, is also carried out. It is shown that optimal allocation of patterns in such cases is a mixed integer programming problem. Using this analysis it is found that equal distribution of training patterns among the processors is not the optimal way to allocate the patterns even when the training set is an integer multiple of the number of processors. Extension of the analysis to processor networks comprising processors of different speeds is also carried out. Experimental results from a T805 transputer array are presented to verify all the theoretical results.

The global increase in greenhouse gas emissions from automobiles has brought about the manufacture and usage of large quantities of electric vehicles (EVs). However, to ensure proper integration of EVs into the grid, there is a need to forecast the charging demand of EVs accurately. This paper presents a short-term electric vehicle charging demand forecast using a feedforward artificial neural network optimized with a modified local leader phase spider monkey optimization (MLLP-SMO) algorithm, a proposed variant of spider monkey optimization. A proportionate fitness selection is employed to improve the update process of the local leader phase of the spider monkey optimization. The proposed algorithm trains a feedforward neural network to forecast electric vehicle charging demand. The effectiveness of the proposed forecasting model was tested and validated with electric vehicle public charging data from the United Kingdom Power Networks Low Carbon London Project. The model's performance was compared to a feedforward neural network trained with particle swarm optimization, genetic algorithm, classical spider monkey optimization, and two conventional forecasting models, multi-linear regression and Monte Carlo simulation. The performance of the proposed forecasting model was assessed using the mean absolute percentage error of forecast and forecasting accuracy. The model produced a forecast accuracy and mean absolute percentage error of 99.88% and 3.384%, respectively. The results show that MLLP-SMO as a trainer predicted better than the other forecasting models and met industry standard forecast accuracy.

On the Circuit Complexity of Sigmoid Feedforward Neural Networks