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Fault diagnosis on Multilevel Inverter (MLI) has been a subject of research for about a decade. This paper is an attempt to deliver a performance analysis of Genetic Algorithm (GA) and the Modified Genetic Algorithm (MGA) working to optimize Artificial Neural Network (ANN) that trains itself on the fault detection and reconfiguration of the Cascaded Multilevel Inverters (CMLI). The open circuit (OC)...
The bearing capacity detection of anchor bolt system is very important for the supporting effect evaluation. In this paper, back propagation neural network(BPNN) and genetic algorithm(GA) were used to predict the pull force of free bolt. Acoustic stress wave signals of free bolt were collected under different pull forces and analyzed in time domain and frequency domain. The wave velocity, fundamental...
In this paper electroencephalography (EEG) patterns are classified using a feedforward neural network trained with a modified genetic algorithm (GA). The objective is to investigate the effects of weight initialization in the neural network and to propose the best settings. Special operators like geometric ranking selection, blend-alpha crossover and non-uniform mutation are employed. For the initialization...
Reasonable network structure can obviously improve the learning speed and generalization ability of BP network. In this paper, an improved method to determine the number of hidden layer neurons is proposed. The method mainly takes the theory of linear correlation analysis to delete the redundant nodes and assign the weights related to others. What's more, genetic algorithm is used to optimize the...
Krill Herd is a new optimization technique that was inspired by the herding behavior of real small crustaceans called Krills. The method was developed for continuous optimization problems and has recently been successfully applied to different complex problems. Feedforward neural network has a number of characteristics which make it suitable for solving complex classification problems. The training...
Image classification is a crucial task in Computer Vision. Feature detection represents a key component of the image classification process, which aims at detecting a set of important features that have the potential to facilitate the classification task. In this paper, we propose a Genetic Programming (GP) approach to image feature detection. The proposed method uses the Speeded Up Robust Features...
Underwater target classification is a very demanding task owing to ever changing complicated nature of the underwater communication channels. Underwater target classification system identifies targets from a mixture of underwater events by its characteristic signature. The characteristic signatures pertaining to each target are patterned by feature recognition algorithms operating on hydrophone captured...
Image hash functions find extensive application in content authentication, database search, and digital forensic. This paper develops a novel robust image-hashing method based on genetic algorithm (GA) and Back Propagation (BP) Neural Network for content authentication. Lifting wavelet transform is used to extract image low frequency coefficients to create the image feature matrix. A GA-BP network...
Geometrical feature assessment of a cancerous tumor embedded in biological soft tissue is a necessity in follow-up procedure and making suitable therapeutic decisions. Evidently by having such features in hand, tumor resections will be more curative and beneficial. In this paper a procedure of examining boundaries of a sphere-shaped tumor embedded in the liver tissue was investigated. At first, the...
Infertility problem is an important issue in recent decades. Semen analysis is one of the principle tasks to evaluate male partner fertility potential. It has been seen in many researches that life habits and health status affect semen quality. Data mining as a decision support system can help to recognize this effect. The artificial neural network (ANN) is a powerful data mining tool that can be...
Recent BCIs mainly need calibration sessions for a new user in order to system training before the usage. Such systems are known as subject-dependent BCIs which are suitable for just one particular subject. In this research, we proposed an efficient subject-independent BCI that can be applicable for any new subject without the need to calibration session in order to train the BCI system. For this...
With the increasing complexity of modern industrial processes and equipment, single fault diagnosis technology has failed to meet diagnostic needs. A complex diagnostic system which get together a variety of different technologies is the future development trend of fault diagnosis. According to a large number of characteristic information caused by difficult fault diagnosis, principal component analysis...
Recently computer systems' call sequences are considered as a data source, this paper expounds how to use Hidden Markov Models (HMM) for software behavior recognition and trend prediction. Due to that HMM is sensitive to initial parameters, especially sensitive to B-parameter which makes model fall into a local optimum in training, this paper proposes using Genetic Algorithm (GA) approach to optimize...
The selection of classifiers which are profitable is becoming more and more important in real-life situations such as customer churn management campaigns in the telecommunication sector. In previous works, the expected maximum profit (EMP) metric has been proposed, which explicitly takes the cost of offer and the customer lifetime value (CLV) of retained customers into account. It thus permits the...
the objective to develop clinical decision support system (CDSS) tools is to help physicians making faster and more reliable clinical decisions. The first step in their development is choose a machine learning classifier as the system core. Previous works reported implementation of artificial neural networks, support vector machines, genetic algorithms, etc. as core classifiers for CDSS; however,...
A Deep Neural Network (DNN) using the same activation function for all hidden neurons has an optimization limitation due to its single mathematical functionality. To solve it, a new DNN with different activation functions is designed to globally optimize both parameters (weights and biases) and function selections. In addition, a novel Genetic Deep Neural Network (GDNN) with different activation functions...
In recent years, with increasing use of internet the computer systems are facing many number of security issues. Intrusion detection system (IDS) is one of the principal components of any information security system. Identification of anomalous activity in computer network is first step in identifying the threat to information system. Our focus is mainly on Genetic algorithm (GA) based anomaly detection...
This paper proposes a new type of filter called neural network filter for image denoising. The noisy images are fed as input to the network and the weights are updated with LMS algorithm. Further the weights are updated by using a recently developed novel optimization technique called Accelerated Particle Swarm Optimization (APSO) for gray level image denoising application. Implementation of APSO...
Military simulations, especially those for personnel training and equipment effectiveness analysis, require proper human behavior models (HBMs) to play blue or red. Traditionally, the HBMs are controlled through rule based scripts. However, the doctrine-driven behavior is rigid and predictable, and more often than not unable to adapt to new situations. In most cases, the subject matter experts (SMEs)...
Stock selection is an important issue when it comes to investing in the stock market. However, it is worth investigating the problem of selecting portfolios while considering not only low risk but also high return on investment. The calculation process of the traditional method is highly complex and is not comprehensive in terms of what it takes into consideration. Hence, this paper proposes a new...
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