scholarly journals RANDOM NEURAL NETWORK LEARNING HEURISTICS

2017 ◽  
Vol 31 (4) ◽  
pp. 436-456 ◽  
Author(s):  
Abbas Javed ◽  
Hadi Larijani ◽  
Ali Ahmadinia ◽  
Rohinton Emmanuel
Keyword(s):  
Neural Network ◽  
Mean Squared Error ◽  
Training Algorithms ◽  
Local Minima ◽  
Neural Network Learning ◽  
Bee Colony ◽  
Squared Error ◽  
Random Neural Network ◽  
Evolution Algorithms ◽  
Learning Heuristics

The random neural network (RNN) is a probabilitsic queueing theory-based model for artificial neural networks, and it requires the use of optimization algorithms for training. Commonly used gradient descent learning algorithms may reside in local minima, evolutionary algorithms can be also used to avoid local minima. Other techniques such as artificial bee colony (ABC), particle swarm optimization (PSO), and differential evolution algorithms also perform well in finding the global minimum but they converge slowly. The sequential quadratic programming (SQP) optimization algorithm can find the optimum neural network weights, but can also get stuck in local minima. We propose to overcome the shortcomings of these various approaches by using hybridized ABC/PSO and SQP. The resulting algorithm is shown to compare favorably with other known techniques for training the RNN. The results show that hybrid ABC learning with SQP outperforms other training algorithms in terms of mean-squared error and normalized root-mean-squared error.

scholarly journals Forecasting Wind Power Generation Using Artificial Neural Network: “Pawan Danawi”—A Case Study from Sri Lanka

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Amila T. Peiris ◽  
Jeevani Jayasinghe ◽  
Upaka Rathnayake
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Wind Power ◽  
Wind Farm ◽  
Mean Squared Error ◽  
Wind Power Generation ◽  
Training Algorithms ◽  
Training Algorithm ◽  
Squared Error ◽  
Ann Models

Wind power, as a renewable energy resource, has taken much attention of the energy authorities in many countries, as it is used as one of the major energy sources to satisfy the ever-increasing energy demand. However, careful attention is needed in identifying the wind power potential in a particular area due to climate changes. In this sense, forecasting both wind power generation and wind power potential is essential. This paper develops artificial neural network (ANN) models to forecast wind power generation in “Pawan Danawi”, a functioning wind farm in Sri Lanka. Wind speed, wind direction, and ambient temperature of the area were used as the independent variable matrices of the developed ANN models, while the generated wind power was used as the dependent variable. The models were tested with three training algorithms, namely, Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) training algorithms. In addition, the model was calibrated for five validation percentages (5% to 25% in 5% intervals) under each algorithm to identify the best training algorithm with the most suitable training and validation percentages. Mean squared error (MSE), coefficient of correlation (R), root mean squared error ratio (RSR), Nash number, and BIAS were used to evaluate the performance of the developed ANN models. Results revealed that all three training algorithms produce acceptable predictions for the power generation in the Pawan Danawi wind farm with R > 0.91, MSE < 0.22, and BIAS < 1. Among them, the LM training algorithm at 70% of training and 5% of validation percentages produces the best forecasting results. The developed models can be effectively used in the prediction of wind power at the Pawan Danawi wind farm. In addition, the models can be used with the projected climatic scenarios in predicting the future wind power harvest. Furthermore, the models can acceptably be used in similar environmental and climatic conditions to identify the wind power potential of the area.

scholarly journals RNN-ABC: A New Swarm Optimization Based Technique for Anomaly Detection

Computers ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 59 ◽  
Author(s):  
Ayyaz-Ul-Haq Qureshi ◽  
Hadi Larijani ◽  
Nhamoinesu Mtetwa ◽  
Abbas Javed ◽  
Jawad Ahmad
Keyword(s):  
Intrusion Detection ◽  
Mean Squared Error ◽  
Detection System ◽  
Data Set ◽  
Bee Colony ◽  
Squared Error ◽  
Random Neural Network ◽  
Prime Concern ◽  
Gradient Descent Algorithm ◽  
Memory Resources

The exponential growth of internet communications and increasing dependency of users upon software-based systems for most essential, everyday applications has raised the importance of network security. As attacks are on the rise, cybersecurity should be considered as a prime concern while developing new networks. In the past, numerous solutions have been proposed for intrusion detection; however, many of them are computationally expensive and require high memory resources. In this paper, we propose a new intrusion detection system using a random neural network and an artificial bee colony algorithm (RNN-ABC). The model is trained and tested with the benchmark NSL-KDD data set. Accuracy and other metrics, such as the sensitivity and specificity of the proposed RNN-ABC, are compared with the traditional gradient descent algorithm-based RNN. While the overall accuracy remains at 95.02%, the performance is also estimated in terms of mean of the mean squared error (MMSE), standard deviation of MSE (SDMSE), best mean squared error (BMSE), and worst mean squared error (WMSE) parameters, which further confirms the superiority of the proposed scheme over the traditional methods.

scholarly journals A Neural Network Approach towards Generalized Resistive Switching Modelling

Micromachines ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1132
Author(s):  
Guilherme Carvalho ◽  
Maria Pereira ◽  
Asal Kiazadeh ◽  
Vítor Grade Tavares
Keyword(s):  
Neural Network ◽  
Resistive Switching ◽  
Network Performance ◽  
Mean Squared Error ◽  
Model Complexity ◽  
Neural Network Approach ◽  
Research Teams ◽  
Neural Network Learning ◽  
Squared Error ◽  
Model Update

Resistive switching behaviour has been demonstrated to be a common characteristic to many materials. In this regard, research teams to date have produced a plethora of different devices exhibiting diverse behaviour, but when system design is considered, finding a ‘one-model-fits-all’ solution can be quite difficult, or even impossible. However, it is in the interest of the community to achieve more general modelling tools for design that allows a quick model update as devices evolve. Laying the grounds with such a principle, this paper presents an artificial neural network learning approach to resistive switching modelling. The efficacy of the method is demonstrated firstly with two simulated devices and secondly with a 4 μm2 amorphous IGZO device. For the amorphous IGZO device, a normalized root-mean-squared error (NRMSE) of 5.66 × 10−3 is achieved with a [2, 50,50 ,1] network structure, representing a good balance between model complexity and accuracy. A brief study on the number of hidden layers and neurons and its effect on network performance is also conducted with the best NRMSE reported at 4.63 × 10−3. The low error rate achieved in both simulated and real-world devices is a good indicator that the presented approach is flexible and can suit multiple device types.

scholarly journals RANDOM NEURAL NETWORK LEARNING HEURISTICS – CORRIGENDUM

2017 ◽  
Vol 32 (3) ◽  
pp. 482-482
Author(s):  
Abbas Javed ◽  
Hadi Larijani ◽  
Ali Ahmadinia ◽  
Rohinton Emmanuel
Keyword(s):  
Neural Network ◽  
Neural Network Learning ◽  
Network Learning ◽  
Random Neural Network ◽  
Learning Heuristics

scholarly journals Penentuan Arsitektur Jaringan Syaraf Tiruan Backpropagation (Bobot Awal dan Bias Awal) Menggunakan Algoritma Genetika

2015 ◽  
Vol 9 (1) ◽  
pp. 77 ◽  
Author(s):  
Christian Dwi Suhendra ◽  
Retantyo Wardoyo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Local Minima ◽  
Initial Weight ◽  
Neural Network Learning ◽  
Squared Error ◽  
Fitness Value ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Backpropagation Learning

AbstrakKelemahan dari jaringan syaraf tiruan backpropagation adalah sangat lama untuk konvergen dan permasalahan lokal mininum yang membuat jaringan syaraf tiruan (JST) sering terjebak pada lokal minimum. Kombinasi parameter arsiktektur, bobot awal dan bias awal yang baik sangat menentukan kemampuan belajar dari JST untuk mengatasi kelemahan dari JST backpropagation.            Pada penelitian Ini dikembangkan sebuah metode untuk menentukan kombinasi parameter arsitektur, bobot awal dan bias awal. Selama ini kombinasi ini dilakukan dengan mencoba kemungkinan satu per satu, baik kombinasi hidden layer pada architecture maupun bobot awal, dan bias awal. Bobot awal dan bias awal digunakan sebagai parameter dalam perhitungan nilai fitness. Ukuran setiap individu terbaik dilihat dari besarnya jumlah kuadrat galat (sum of squared error = SSE) masing – masing individu, individu dengan SSE terkecil merupakan individu terbaik. Kombinasi parameter arsiktektur, bobot awal dan bias awal yang terbaik akan digunakan sebagai parameter dalam pelatihan JST backpropagation.Hasil dari penelitian ini adalah sebuah solusi alternatif untuk menyelesaikan permasalahan pada pembelajaran backpropagation yang sering mengalami masalah dalam penentuan parameter pembelajaran. Hasil penelitian ini menunjukan bahwa metode algoritma genetika dapat memberikan solusi bagi pembelajaran backpropagation dan memberikan tingkat akurasi yang lebih baik, serta menurunkan lama pembelajaran jika dibandingkan dengan penentuan parameter yang dilakukan secara manual. Kata kunci  Jaringan syaraf tiruan, algoritma genetika, backpropagation, SSE, lokal minimum AbstractThe weakness of back propagation neural network is very slow to converge and local minima issues that makes artificial neural networks (ANN) are often being trapped in a local minima. A good combination between architecture, intial weight and bias are so important to overcome the weakness of backpropagation neural network.This study developed a method to determine the combination parameter of architectur, initial weight and bias. So far, trial and error is commonly used to select the combination of hidden layer, intial weight and bias. Initial weight and bias is used as a parameter in order to evaluate fitness value. Sum of squared error(SSE) is used to determine best individual. individual with the smallest SSE is the best individual. Best combination parameter of architecture, initial weight and bias will be used as a paramater in the backpropagation neural network learning.            The results of this study is an alternative solution to solve the problems on the backpropagation learning that often have problems in determining the parameters of the learning. The result shows genetic algorithm method can provide a solution for backpropagation learning and can improve the accuracy, also reduce long learning when it compared with the parameters were determined manually. Keywords: Artificial neural network, genetic algorithm, backpropagation, SSE, local minima.

scholarly journals Prediksi Indeks Harga Saham Gabungan (IHSG) Menggunakan Algoritma Neural Network

2018 ◽  
Vol 4 (1) ◽  
pp. 24
Author(s):  
Imam Halimi ◽  
Wahyu Andhyka Kusuma
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Linear Regression ◽  
Mean Squared Error ◽  
Composite Index ◽  
T Test ◽  
Sliding Windows ◽  
Root Mean Squared Error ◽  
Squared Error

Investasi saham merupakan hal yang tidak asing didengar maupun dilakukan. Ada berbagai macam saham di Indonesia, salah satunya adalah Indeks Harga Saham Gabungan (IHSG) atau dalam bahasa inggris disebut Indonesia Composite Index, ICI, atau IDX Composite. IHSG merupakan parameter penting yang dipertimbangkan pada saat akan melakukan investasi mengingat IHSG adalah saham gabungan. Penelitian ini bertujuan memprediksi pergerakan IHSG dengan teknik data mining menggunakan algoritma neural network dan dibandingkan dengan algoritma linear regression, yang dapat dijadikan acuan investor saat akan melakukan investasi. Hasil dari penelitian ini berupa nilai Root Mean Squared Error (RMSE) serta label tambahan angka hasil prediksi yang didapatkan setelah dilakukan validasi menggunakan sliding windows validation dengan hasil paling baik yaitu pada pengujian yang menggunakan algoritma neural network yang menggunakan windowing yaitu sebesar 37,786 dan pada pengujian yang tidak menggunakan windowing sebesar 13,597 dan untuk pengujian algoritma linear regression yang menggunakan windowing yaitu sebesar 35,026 dan pengujian yang tidak menggunakan windowing sebesar 12,657. Setelah dilakukan pengujian T-Test menunjukan bahwa pengujian menggunakan neural network yang dibandingkan dengan linear regression memiliki hasil yang tidak signifikan dengan nilai T-Test untuk pengujian dengan windowing dan tanpa windowing hasilnya sama, yaitu sebesar 1,000.

scholarly journals Modelling of Horizontal Flexible Plate Structure Using Artificial Bee Colony Algorithm

2018 ◽  
Vol 7 (4.36) ◽  
pp. 415
Author(s):  
Muhamad Sukri Hadi ◽  
Sukri Hadi Zaurah Mat Darus
Keyword(s):  
System Identification ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Mean Squared Error ◽  
Least Square ◽  
Flexible Plate ◽  
Plate Structure ◽  
Bee Colony ◽  
Squared Error ◽  
Plate System

This paper presents the performance of system identification for modeling the horizontal flexible plate system using artificial bee colony and recursive least square algorithms. Initially, the experimental rig of flexible plate was designed and fabricated with all edges clamped boundary condition at the horizontal position. Then, the instrumentation and data acquisition systems were integrated into the rig for acquiring the input-output vibration experimentally. The collected data in the experiment will be used later for modeling the dynamic system of horizontal flexible plate system using system identification. The effectiveness of the developed model will be validated using mean squared error, one step ahead prediction, correlation tests and pole zero diagram stability. The estimated of the developed models were found are acceptable and possible to be used as a platform of controller development for vibration suppression of the undesirable vibration in the flexible plate structure. It was found that the artificial bee colony algorithm has performed better in this study by achieving the lowest mean squared error, good correlation test and high stability in the pole zero diagram.  

scholarly journals PENENTUAN PARAMETER LEARNING RATE SELAMA PEMBELAJARAN JARINGAN SYARAF TIRUAN BACKPROPAGATION MENGGUNAKAN ALGORITMA GENETIKA

2020 ◽  
Vol 14 (2) ◽  
pp. 202-212
Author(s):  
Ade chandra Saputra
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fitness Function ◽  
Learning Rate ◽  
Local Minima ◽  
Rate Parameter ◽  
Neural Network Learning ◽  
Value Of Learning ◽  
Backpropagation Learning

One of the weakness in backpropagation Artificial neural network(ANN) is being stuck in local minima. Learning rate parameter is an important parameter in order to determine how fast the ANN Learning. This research is conducted to determine a method of finding the value of learning rate parameter using a genetic algorithm when neural network learning stops and the error value is not reached the stopping criteria or has not reached the convergence. Genetic algorithm is used to determine the value of learning rate used is based on the calculation of the fitness function with the input of the ANN weights, gradient error, and bias. The calculation of the fitness function will produce an error value of each learning rate which represents each candidate solutions or individual genetic algorithms. Each individual is determined by sum of squared error value. One with the smallest SSE is the best individual. The value of learning rate has chosen will be used to continue learning so that it can lower the value of the error or speed up the learning towards convergence. The final result of this study is to provide a new solution to resolve the problem in the backpropagation learning that often have problems in determining the learning parameters. These results indicate that the method of genetic algorithms can provide a solution for backpropagation learning in order to decrease the value of SSE when learning of ANN has been static in large error conditions, or stuck in local minima

Design of Intelligent Network to Predicate Phenol Removal from Waste Water by Emulsion Liquid Membrane

2021 ◽  
Vol 1021 ◽  
pp. 115-128
Author(s):  
Suheila Abd Alreda Akkar ◽  
Sawsan Abd Muslim Mohammed
Keyword(s):  
Neural Network ◽  
Liquid Membrane ◽  
Mean Squared Error ◽  
Back Propagation ◽  
Volume Ratio ◽  
Phenol Removal ◽  
Acid Extraction ◽  
Training Algorithms ◽  
Membrane Phase ◽  
Emulsion Liquid Membrane

This research introduced Intelligent Network's proposed design for predicting efficiency in the removal of phenol from wastewater by liquid membrane emulsion. In the inner phase of W / O emulsions, phenol extraction from an aqueous solution was investigated using emulsion liquid membrane prepared with kerosene as a membrane phase, Span 80 as a surfactant, and NaOH as a stripping agent. Experiments were conducted to investigate the effect of three emulsion composition variables, namely: surfactant concentration, membrane phase to-internal (VM / VI) volume ratio, and removal phase concentration in the internal phase, and two process parameters, feed phase agitation speed at organic acid extraction rates, and emulsion-to-feed volume ratio (VE / VF). More than 98% of phenol can be extracted in less than 5 minutes. This article describes compares the performance of different learning algorithms such as GD, RB, GDM, GDX, CG, and LM to predict the efficiency of phenol removal from wastewater through the liquid emulsion membrane. The proposed neural network consisted of (7, 11, 1) neurons in the input , hidden and output layers respectively feed forward ANN with various types of back propagation training algorithms were developed to model the emulsion liquid membrane removal of phenols. The values predicted for the neural network model are found in close agreement with the results of the batch experiment using MATLAB program with a correlation coefficient ( R2) of 0.999 and Mean Squared Error (MSE) of 0.004.

Identification of Complex System Based on Neural Network

2012 ◽  
Vol 433-440 ◽  
pp. 4342-4347
Author(s):  
Zhen Hai Dou ◽  
Ya Jing Wang
Keyword(s):  
Neural Network ◽  
Complex System ◽  
Mean Squared Error ◽  
Model Identification ◽  
Complex Model ◽  
System Model ◽  
Mathematics Model ◽  
Identification Method ◽  
Squared Error ◽  
Actual Output

In order to conquer the difficulty of building up the mathematics model of some complex system, model identification method based on neural network is put forward. By this method, according to actual sample datum, the complex model of crude oil heating furnace is identified at appropriate quantity of net layers and notes. The identification results show that output of model can basically consistent with the actual output and their mean squared error (MSE) almost is 0. Therefore, model identification method based on neural network is an effective method in complex system identification.

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