scholarly journals Quantum inspiration to build a neural model based on the Day-Ahead Market of the Polish Power Exchange

2021 ◽  
Author(s):  
Dariusz Ruciński
Keyword(s):  
Methodological Approach ◽  
Neural Model ◽  
Neural Modeling ◽  
Density Matrices ◽  
Processing Elements ◽  
Power Exchange ◽  
Matrix Calculus ◽  
Comparative Model ◽  
Hidden Layer ◽  
Vector Matrix

The article is an attempt of the methodological approach to the proposed quantum-inspired method of neural modeling of prices quoted on the Day-Ahead Market operating at TGE S.A. In the proposed quantum-inspired neural model it was assumed, inter alia, that it is composed of 12 parallel Perceptron ANNs with one hidden layer. Moreover, it was assumed that weights and biases as processing elements are described by density matrices, and the values flowing through the Artificial Neural Network of Signals are represented by qubits. Calculations checking the correctness of the adopted method and model were carried out with the use of linear algebra and vector-matrix calculus in MATLAB and Simulink environments. The obtained research results were compared to the results obtained from the neural model with the use of a comparative model.

scholarly journals To contemplate quantitative and qualitative water features by neural networks method

2011 ◽  
Vol 48 (No. 7) ◽  
pp. 322-326 ◽  
Author(s):  
M. Neruda ◽  
R. Neruda
Keyword(s):  
Neural Networks ◽  
Fourier Series ◽  
Computer Science ◽  
Hybrid System ◽  
Back Propagation ◽  
Neural Model ◽  
Black Box ◽  
Layer Number ◽  
Accurate Forecast ◽  
Hidden Layer

An application deals with calibration of neural model and Fourier series model for Ploučnice catchment. This approach has an advantage, that the network choice is independent of other example’s parameters. Each networks, and their variants (different units and hidden layer number) can be connected in as a black box and tested independently. A Stuttgart neural simulator SNNS and a multiagent hybrid system Bang2 developed in Institute of Computer Science, AS CR have been used for testing. A perceptron network has been constructed, which was trained by back propagation method improved with a momentum term. The network is capable of an accurate forecast of the next day runoff based on the runoff and rainfall values from previous day.

scholarly journals EVOLUTIONARY ALGORITHM INSPIRED BY THE METHODS OF QUANTUM COMPUTER SCIENCES FOR THE IMPROVEMENT OF A NEURAL MODEL OF THE ELECTRIC POWER EXCHANGE

2017 ◽  
Vol 6 (4) ◽  
pp. 343-355 ◽  
Author(s):  
Jerzy Tchórzewski
Keyword(s):  
Evolutionary Algorithm ◽  
Electric Power ◽  
Quantum Computer ◽  
Electrical Energy ◽  
Neural Model ◽  
Initial Population ◽  
Mutation Operators ◽  
Power Exchange ◽  
Using Data ◽  
Crossover And Mutation

The work contains results of research on the possibility to improve the neural model of the Electric Power Exchange (polish: Towarowa Giełda Energii Elektrycznej – TGEE) in MATLAB and Simulink environment using evolutionary algorithm inspired by quantum computer science. The developed artificial neural network was trained using data for the Day Ahead Market, assuming the joint volume of supplied and sold electrical energy [MWh] as the input quantities in each hour of the 24-hour day, and average prices [PLN/MWh] as output quantities. The obtained model of the exchange system was improved using the evolutionary algorithm, and further improvement in the accuracy of the model by supplementing the evolutionary algorithm using quantum solutions, related to the initial population, crossover and mutation operators, selection, etc. were proposed.

scholarly journals Analisis Kritis terhadap Spirit Dekonstruksi dalam Kajian Hermeneutika Kristen Kontemporer

2021 ◽  
Vol 20 (1) ◽  
pp. 1
Author(s):  
Ferry Simanjuntak ◽  
Yosep Belay
Keyword(s):  
Methodological Approach ◽  
The Body ◽  
Christian Theology ◽  
Literature Study ◽  
Analysis Pattern ◽  
Contemporary Christian ◽  
Theological Discourse ◽  
Comparative Model ◽  
The Impact ◽  
Christian Discourse

<p><em>This paper aims to analyze the impact of Derrida's theory of deconstruction in relation to the application of contemporary Christian hermeneutics as well as an attempt at hermeneutical repositioning. The methodological approach used in this paper is descriptive qualitative with instruments of literature study, comparison and textual analysis. Concretely, critical analysis is carried out in stages of deconstruction theory, various phenomena of contemporary Christian hermeneutics, then presents the idea of Christian hermeneutics as a comparative model and discourse criticism. Meanwhile, the body of the writing is divided into three parts according to the analysis pattern. First, it specifically examines Derrida's theory of deconstruction. Second, it is an analysis of several forms of hermeneutic phenomena and discourse of Christian theology which are currently developing. Third, reviewing the Christian hermeneutic discourse from the evangelical perspective in an effort to reposition, criticize, and test discourse on the contemporary worldview. Through this research, distortions were found in hermeneutic studies and contemporary Christian discourse with several forms of deconstruction approaches. The three explicit patterns used are the hermeneutic application of binary negation to conservative theological discourse, the explicit emphasis on the textual eisegesis model and the post-structuralism approach to interpretation..</em><strong></strong></p><p><strong>Key words: </strong>Dekonstruksi, hermeneutika, oposisi biner, semantic, interpretasi biblis</p>

scholarly journals Application of artificial neural network for the quality-based classification of spray-dried rhubarb juice powders

2020 ◽  
Author(s):  
K. Przybył ◽  
J. Gawałek ◽  
K. Koszela
Keyword(s):  
Principal Component ◽  
Neural Model ◽  
Significance Level ◽  
Image Descriptors ◽  
Graphic Data ◽  
Color Features ◽  
Input Variables ◽  
Hidden Layer ◽  
Spray Dried

Abstract The aim of the study was to develop a neural model enabling classification of fruit spray dried powders, on the basis of graphic data acquired from a bitmap received in the process of spray drying. The neural model was developed with multi-layer perceptron topology. Input variables were expressed in 46 image descriptors based on RGB, YCbCr, HSV (B) and HSL models. Sensitivity analysis of input variables and principal component analysis determined the significance level of each attribute. The optimal model with the lowest error value root mean square, at the level of 0.04 contained 46 neurons in the input layer, 11 neurons in the hidden layer, 10 neurons in the output layer. The results allowed to show that dyeing force (color features) had influence on effective differentiation of the research material consisting of spray-dried powders of rhubarb juice with various dried juice content levels: 30, 40 and 50% as well as high (“H”) and low (“L”) level of saccharification a chosen carrier (potato maltodextrin).

Development of a Neural Statistical Model for the Relative Humidity Levels Prediction in the Region of Rabat-Kenitra (Morocco)

2021 ◽  
Author(s):  
Kaoutar Elazhari ◽  
Badreddine ABDALLAOUI ◽  
Ali DEHBI ◽  
Abdelaziz ABDALLAOUI ◽  
Hamid ZINEDDINE
Keyword(s):  
Relative Humidity ◽  
Meteorological Parameters ◽  
Transfer Functions ◽  
Mean Squared Error ◽  
Neural Model ◽  
Ann Model ◽  
History Of ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Mlp Model

Abstract This work provides the development of a powerful artificial neural network (ANN) model, for the prediction of relative humidity levels, using other meteorological parameters of the Rabat-Kenitra region. The treatment was applied to a database containing a daily history of five meteorological parameters of 9 stations covering this region for a period from 1979 to mid-2014. We have shown that for the prediction of relative humidity in this region, the best performing three-layer ANN (input, hidden and output) mathematical model is the multi-layer perceptron (MLP) model. This neural model using the Levenberg-Marquard algorithm, having an architecture [5-11-1] and the transfer functions Tansig in the hidden layer and Purelin in the output layer was able to estimate values for relative humidity very close to those observed. Indeed, this was affirmed by a low mean squared error (MSE) and a fairly high correlation coefficient (R), compared to the statistical indicators relating to the other models developed as part of this study.

Applicability of ANN in Adsorptive Removal of Cd(II) from Aqueous Solution

2016 ◽  
pp. 523-560
Author(s):  
Nirjhar Bar ◽  
Sudip Kumar Das
Keyword(s):  
Blood Cells ◽  
Transfer Functions ◽  
Ann Model ◽  
Renal Disorder ◽  
Conjugate Gradient Algorithms ◽  
Processing Elements ◽  
Gradient Algorithms ◽  
Percentage Removal ◽  
Hidden Layer ◽  
User Friendly

Cadmium is frequently used and is extremely toxic in relatively low dosages and is one of the principal heavy metals that is responsible for causing kidney damage, high blood pressure, renal disorder, destruction of red blood Cells and bone fracture. Permissible limit to discharge in the inland surface water is 2.0 mg/l, discharge in public sewers is 1.0 mg/l and drinking water is 0.01 mg/l. Adsorption is the only user-friendly technique for the removal of heavy metal. We have developed an ANN model for prediction of percentage removal of Cd(II). A multilayer perceptron with a single hidden layer has been learnt separately by three different algorithms: Backpropagation, Levenberg-Marquardt and Scaled Conjugate Gradient algorithms for analysis purpose. Optimization for each one of the four standard transfer functions (in a single hidden layer) has been carried out in all three cases. The ANN model with Backpropagation algorithm, with the second transfer function and 25 processing elements gives the best predictability of the outlet concentration.

scholarly journals QUANTUM-INSPIRED ARTIFICIL NEURAL NETWORKS AND EVOLUTIONARY ALGORITHMS METHODS APPLIED TO MODELING OF THE POLISH ELECTRIC POWER EXCHANGE USING THE DAY-AHEAD MARKET DATA

2018 ◽  
Vol 7 (3) ◽  
pp. 201-212
Author(s):  
Jerzy Tchórzewski ◽  
Dariusz Ruciński
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Quantum Computing ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Computer Science ◽  
Electric Power ◽  
Quantum Computer ◽  
Neural Model ◽  
Power Exchange

The paper presents selected results of research on the use of artificial intelligence methods, which are inspired by quantum computing solutions for modelling of electric power exchange systems. Methods used in the modelling of quantum data acquisition, quantization and dequantization of information as well as the methods of performing quantum computations were emphasized. Furthermore, we have analysed the results obtained for the neural model and for the evolutionary algorithm inspired by the quantum computer science. Eventually, the model was verified on the example of the neural model of the Electric Power Exchange (EPE).

An Application of a Recurrent Neural Model for Parsing Natural Language

1997 ◽  
Vol 06 (03) ◽  
pp. 397-419
Author(s):  
John J. Salerno
Keyword(s):  
Learning Algorithm ◽  
Neural Model ◽  
Error Rates ◽  
Training Phase ◽  
Initial Training ◽  
Neural Models ◽  
Fixed Size ◽  
Small Set ◽  
Hidden Layer ◽  
Basic Learning

Neural models for dealing with symbolic processing are in their infancy. Success thus far can be defined by the parsing of very simple phrases and a small set of words into small, fixed size frames. Many of these systems do not scale well as one increases the number of words or the phrase length. These models are limited with respect to the large number of epochs required to train and the error rates. In the discussion that follows we will address the issue of training. We will present an analysis which will provide a lower bound on the error rate. The approach presents simple extensions to the basic learning algorithm and make use of a closest neighbor algorithm for correctness. Other issues such as generalization versus memorization, optimum hidden layer size and teaching the network after the initial training phase are also discussed.

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