364 research outputs found
Improved cuckoo search based neural network learning algorithms for data classification
Artificial Neural Networks (ANN) techniques, mostly Back-Propagation Neural
Network (BPNN) algorithm has been used as a tool for recognizing a mapping
function among a known set of input and output examples. These networks can be
trained with gradient descent back propagation. The algorithm is not definite in
finding the global minimum of the error function since gradient descent may get
stuck in local minima, where it may stay indefinitely. Among the conventional
methods, some researchers prefer Levenberg-Marquardt (LM) because of its
convergence speed and performance. On the other hand, LM algorithms which are
derivative based algorithms still face a risk of getting stuck in local minima.
Recently, a novel meta-heuristic search technique called cuckoo search (CS)
has gained a great deal of attention from researchers due to its efficient convergence
towards optimal solution. But Cuckoo search is prone to less optimal solution during
exploration and exploitation process due to large step lengths taken by CS due to
Levy flight. It can also be used to improve the balance between exploration and
exploitation of CS algorithm, and to increase the chances of the egg’s survival.
This research proposed an improved CS called hybrid Accelerated Cuckoo
Particle Swarm Optimization algorithm (HACPSO) with Accelerated particle Swarm
Optimization (APSO) algorithm. In the proposed HACPSO algorithm, initially
accelerated particle swarm optimization (APSO) algorithm searches within the
search space and finds the best sub-search space, and then the CS selects the best
nest by traversing the sub-search space. This exploration and exploitation method
followed in the proposed HACPSO algorithm makes it to converge to global optima
with more efficiency than the original Cuckoo Search (CS) algorithm. Finally, the proposed CS hybrid variants such as; HACPSO, HACPSO-BP,
HACPSO-LM, CSBP, CSLM, CSERN, and CSLMERN are evaluated and compared
with conventional Back propagation Neural Network (BPNN), Artificial Bee Colony
Neural Network (ABCNN), Artificial Bee Colony Back propagation algorithm
(ABC-BP), and Artificial Bee Colony Levenberg-Marquardt algorithm (ABC-LM).
Specifically, 6 benchmark classification datasets are used for training the hybrid
Artificial Neural Network algorithms. Overall from the simulation results, it is
realized that the proposed CS based NN algorithms performs better than all other
proposed and conventional models in terms of CPU Time, MSE, SD and accuracy
Training a Feed-forward Neural Network with Artificial Bee Colony Based Backpropagation Method
Back-propagation algorithm is one of the most widely used and popular
techniques to optimize the feed forward neural network training. Nature
inspired meta-heuristic algorithms also provide derivative-free solution to
optimize complex problem. Artificial bee colony algorithm is a nature inspired
meta-heuristic algorithm, mimicking the foraging or food source searching
behaviour of bees in a bee colony and this algorithm is implemented in several
applications for an improved optimized outcome. The proposed method in this
paper includes an improved artificial bee colony algorithm based
back-propagation neural network training method for fast and improved
convergence rate of the hybrid neural network learning method. The result is
analysed with the genetic algorithm based back-propagation method, and it is
another hybridized procedure of its kind. Analysis is performed over standard
data sets, reflecting the light of efficiency of proposed method in terms of
convergence speed and rate.Comment: 14 Pages, 11 figure
An improved data classification framework based on fractional particle swarm optimization
Particle Swarm Optimization (PSO) is a population based stochastic optimization technique which consist of particles that move collectively in iterations to search for the most optimum solutions. However, conventional PSO is prone to lack of convergence and even stagnation in complex high dimensional-search problems with multiple local optima. Therefore, this research proposed an improved Mutually-Optimized Fractional PSO (MOFPSO) algorithm based on fractional derivatives and small step lengths to ensure convergence to global optima by supplying a fine balance between exploration and exploitation. The proposed algorithm is tested and verified for optimization performance comparison on ten benchmark functions against six existing established algorithms in terms of Mean of Error and Standard Deviation values. The proposed MOFPSO algorithm demonstrated lowest Mean of Error values during the optimization on all benchmark functions through all 30 runs (Ackley = 0.2, Rosenbrock = 0.2, Bohachevsky = 9.36E-06, Easom = -0.95, Griewank = 0.01, Rastrigin = 2.5E-03, Schaffer = 1.31E-06, Schwefel 1.2 = 3.2E-05, Sphere = 8.36E-03, Step = 0). Furthermore, the proposed MOFPSO algorithm is hybridized with Back-Propagation (BP), Elman Recurrent Neural Networks (RNN) and Levenberg-Marquardt (LM) Artificial Neural Networks (ANNs) to propose an enhanced data classification framework, especially for data classification applications. The proposed classification framework is then evaluated for classification accuracy, computational time and Mean Squared Error on five benchmark datasets against seven existing techniques. It can be concluded from the simulation results that the proposed MOFPSO-ERNN classification algorithm demonstrated good classification performance in terms of classification accuracy (Breast Cancer = 99.01%, EEG = 99.99%, PIMA Indian Diabetes = 99.37%, Iris = 99.6%, Thyroid = 99.88%) as compared to the existing hybrid classification techniques. Hence, the proposed technique can be employed to improve the overall classification accuracy and reduce the computational time in data classification applications
Search Engine Optimization Algorithms for Page Ranking: Comparative Study
Every second, the number of visitors increase day by day due to the fast growing of World Wide Web. Till this day there are more than 11.3 billion web pages in the World Wide Web. In the modern era of technology and advance computation, world page ranking become a common feature of modern retrieval system. However, any query in search engine will display both relevant and irrelevant data that can cause overhead to the search engine and will affect the page ranking process. A new optimization technique is needed to improve the existing search engine optimization in increasing the page ranking. This paper presents a comparative study of different page ranking algorithms for search engine optimization. Also it explores some improvements that are needed to overcome the current problem in this field. The simulation result’s analysis clearly shows that there is a need of new optimization technique. This new technique must reduce the complexity and user overhead by displaying only related data which will reduce overheading in search engine
Impact of noise on a dynamical system: prediction and uncertainties from a swarm-optimized neural network
In this study, an artificial neural network (ANN) based on particle swarm
optimization (PSO) was developed for the time series prediction. The hybrid
ANN+PSO algorithm was applied on Mackey--Glass chaotic time series in the
short-term . The performance prediction was evaluated and compared with
another studies available in the literature. Also, we presented properties of
the dynamical system via the study of chaotic behaviour obtained from the
predicted time series. Next, the hybrid ANN+PSO algorithm was complemented with
a Gaussian stochastic procedure (called {\it stochastic} hybrid ANN+PSO) in
order to obtain a new estimator of the predictions, which also allowed us to
compute uncertainties of predictions for noisy Mackey--Glass chaotic time
series. Thus, we studied the impact of noise for several cases with a white
noise level () from 0.01 to 0.1.Comment: 11 pages, 8 figure
Application of a PSO-based neural network in analysis of outcomes of construction claims
Author name used in this publication: K. W. Chau2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
Artificial intelligence models for refrigeration, air conditioning and heat pump systems
Artificial intelligence (AI) models for refrigeration, heat pumps, and air conditioners have emerged in recent decades. The universal approximation accuracy and prediction performances of various AI structures like feedforward neural networks, radial basis function neural networks, adaptive neuro�fuzzy inference and recurrent neural networks are encouraging interest. This review discusses existing
topographies of neural network models for RHVAC system modelling, energy prediction and fault(s), and detection and diagnosis. Studies show that AI structures require standardization and improvement for tuning hyperparameters (like weight, bias, activation functions, number of hidden layers and neurons). The selection of activation functions, validation, and learning algorithms depends on author’s
suitability for a particular application. Backpropagation, error trial selection of the number of hidden layer, and hidden layers’ neurons, and Levenberg–Marquardt learning algorithms, remain prevalent
methodologies for developing AI structures. The major limitations to the application of AI models in
RHVAC systems include exploding or/and vanishing gradients, interpretability, and accuracy trade off,
and training saturation and limited sensitivity. This review aims to give up-to-date applications of
different AI architectures in RHVAC systems and to identify the associated limitations and prospect
Classification of Abandoned Areas for Solar Energy Projects Using Artificial Intelligence and Quantum Mechanics
The increasing demand for energy has intensified recently, requiring alternative sources to fossil fuels, which have become economically and environmentally unfeasible. On the other hand, the increasing land occupation in recent centuries is a growing problem, demanding greater efficiency, particularly in the reuse of abandoned areas, which has become an alternative. An interesting alternative would be installing energy facilities like solar, wind, biomass, and geothermal, in these areas. The objective of this paper is to develop a classification methodology, based on Artificial Intelligence (AI) and Quantum Theory (QT), to automatically carry out the classification of abandoned areas suitable for the settlement of these power plants. Artificial Neural Networks (ANNs) improved by the hybrid algorithm Quantum-behaved Particle Swarm Optimization (QPSO) together with the Levenberg-Marquardt Algorithm (LMA) were used for the classification task. In terms of Mean Squared Error (MSE), the QPSO-LMA approach achieved a decrease of 19.6% in relation to the classical LMA training with random initial weights. Moreover, the model’s accuracy showed an increase of 7.3% for the QPSO-LMA over the LMA. To validate this new approach, it was also tested on six different datasets available in the UCI Machine Learning Repository and seven classical techniques established in the literature. For the problem of installing photovoltaic plants in abandoned areas, the knowledge acquired with the solar dataset can be extrapolated to other regions
Storage Capacity Estimation of Commercial Scale Injection and Storage of CO2 in the Jacksonburg-Stringtown Oil Field, West Virginia
Geological capture, utilization and storage (CCUS) of carbon dioxide (CO2) in depleted oil and gas reservoirs is one method to reduce greenhouse gas emissions with enhanced oil recovery (EOR) and extending the life of the field. Therefore CCUS coupled with EOR is considered to be an economic approach to demonstration of commercial-scale injection and storage of anthropogenic CO2. Several critical issues should be taken into account prior to injecting large volumes of CO2, such as storage capacity, project duration and long-term containment. Reservoir characterization and 3D geological modeling are the best way to estimate the theoretical CO 2 storage capacity in mature oil fields. The Jacksonburg-Stringtown field, located in northwestern West Virginia, has produced over 22 million barrels of oil (MMBO) since 1895. The sandstone of the Late Devonian Gordon Stray is the primary reservoir.;The Upper Devonian fluvial sandstone reservoirs in Jacksonburg-Stringtown oil field, which has produced over 22 million barrels of oil since 1895, are an ideal candidate for CO2 sequestration coupled with EOR. Supercritical depth (\u3e2500 ft.), minimum miscible pressure (941 psi), favorable API gravity (46.5°) and good water flood response are indicators that facilitate CO 2-EOR operations. Moreover, Jacksonburg-Stringtown oil field is adjacent to a large concentration of CO2 sources located along the Ohio River that could potentially supply enough CO2 for sequestration and EOR without constructing new pipeline facilities.;Permeability evaluation is a critical parameter to understand the subsurface fluid flow and reservoir management for primary and enhanced hydrocarbon recovery and efficient carbon storage. In this study, a rapid, robust and cost-effective artificial neural network (ANN) model is constructed to predict permeability using the model\u27s strong ability to recognize the possible interrelationships between input and output variables. Two commonly available conventional well logs, gamma ray and bulk density, and three logs derived variables, the slope of GR, the slope of bulk density and Vsh were selected as input parameters and permeability was selected as desired output parameter to train and test an artificial neural network. The results indicate that the ANN model can be applied effectively in permeability prediction.;Porosity is another fundamental property that characterizes the storage capability of fluid and gas bearing formations in a reservoir. In this study, a support vector machine (SVM) with mixed kernels function (MKF) is utilized to construct the relationship between limited conventional well log suites and sparse core data. The input parameters for SVM model consist of core porosity values and the same log suite as ANN\u27s input parameters, and porosity is the desired output. Compared with results from the SVM model with a single kernel function, mixed kernel function based SVM model provide more accurate porosity prediction values.;Base on the well log analysis, four reservoir subunits within a marine-dominated estuarine depositional system are defined: barrier sand, central bay shale, tidal channels and fluvial channel subunits. A 3-D geological model, which is used to estimate theoretical CO2 sequestration capacity, is constructed with the integration of core data, wireline log data and geological background knowledge. Depending on the proposed 3-D geological model, the best regions for coupled CCUS-EOR are located in southern portions of the field, and the estimated CO2 theoretical storage capacity for Jacksonburg-Stringtown oil field vary between 24 to 383 million metric tons. The estimation results of CO2 sequestration and EOR potential indicate that the Jacksonburg-Stringtown oilfield has significant potential for CO2 storage and value-added EOR
Artificial Intelligence in Materials Modeling and Design
In recent decades, the use of artificial intelligence (AI) techniques in the field of materials modeling has received significant attention owing to their excellent ability to analyze a vast amount of data and reveal correlations between several complex interrelated phenomena. In this review paper, we summarize recent advances in the applications of AI techniques for numerical modeling of different types of materials. AI techniques such as machine learning and deep learning show great advantages and potential for predicting important mechanical properties of materials and reveal how changes in certain principal parameters affect the overall behavior of engineering materials. Furthermore, in this review, we show that the application of AI techniques can significantly help to improve the design and optimize the properties of future advanced engineering materials. Finally, a perspective on the challenges and prospects of the applications of AI techniques for material modeling is presented
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