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A novel approach for data mining of steam turbine based on neural network and genetic algorithm is brought forward, aimed at overcoming shortages of some current knowledge attaining methods. The historical fault data of steam turbine is processed with fuzzy and discrete method firstly, a multiplayer backpropagation neural network is structured secondly, the neural network is trained via teacherpsilas...
This paper introduces a novel paradigm to impute missing data that combines a decision tree with an auto-associative neural network (AANN) based model and a principal component analysis-neural network (PCA-NN) based model. For each model, the decision tree is used to predict search bounds for a genetic algorithm that minimise an error function derived from the respective model. The modelspsila ability...
Computational intelligence and other data mining techniques are used for characterizing regional and time-varying climatic variations in Spain in the period 1901-2005. Daily maximum temperature data from 10 climatic stations are analyzed (with and without missing values) using principal components (PC), similarity-preservation feature generation, clustering, Kolmogorov-Smirnov dissimilarity analysis...
In this paper, the theory of sparse decomposition is introduced to weak signal detection, and the improved matching pursuit (MP) algorithm is studied to accomplish anti-interference process of some typical signals, such as a weak sine wave signal submerged in strong noises. The improved matching pursuit algorithm uses dual-parameter Gabor dictionary, and the iterative times can be modified in accordance...
We describe in this paper a comparative study of fuzzy inference systems as methods of integration in modular neural networks (MNNpsilas) for multimodal biometry. These methods of integration are based on type-1 and type-2 fuzzy logic. Also, the fuzzy systems are optimized with simple genetic algorithms. First, we considered the use of type-1 fuzzy logic and later the approach with type-2 fuzzy logic...
The multiresolution analysis learning algorithm (MRAL) for neural networks is proposed to get a more precious model from the noisy data set, which based on Multiresolution Analysis (MRA) of the wavelet transformation and nondominated sorting genetic algorithm-II (NSGA-II). Several different scaled signals of the error function are used as the objections, and NSGA-II algorithm is applied to optimize...
We address the problem of selecting and extracting key features by using singular value decomposition and latent semantic analysis. As a consequence, we are able to discover latent information which allows us to design signatures for forensics and in a dual approach for real-time intrusion detection systems. The validity of this method is shown by using several automated classification algorithms...
In this paper, statistical experimental design is used to characterize the contact formation process for high-performance silicon solar cells. Central composite design is employed, and neural networks trained by the error back-propagation algorithm are used to model the relationships between several input factors and solar cell efficiency. Subsequently, both genetic algorithms and particle swarm optimization...
We describe in this paper a new hybrid approach for optimization combining particle swarm optimization (PSO) and genetic algorithms (GAs) using fuzzy logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved PSO+GA hybrid method fuzzy logic is used to combine the results of the PSO and GA in the best way possible. The new hybrid PSO+GA...
This paper investigates the problem of feature subset selection as part of a methodology that integrates heuristic tabu search, simulated annealing, genetic algorithms and backpropagation. This technique combines both global and local search strategies for the simultaneous optimization of the number of connections and connection values of multi-layer perceptron neural networks. We compare the performance...
This paper presents an eclectic method for extracting simple classification rules using a combination of a genetic algorithm, a self-organizing map and the ID3 decision tree algorithm. After outlining the method for extracting rules, we assess them for effectiveness, complexity and precision and compare them with similar methods which use support vector machines. While it is no surprise that the method...
This paper describes an improved version of a method that automatically searches near-optimal multi-layer feedforward artificial neural networks using genetic algorithms. This method employs an evolutionary search for simultaneous choices of initial weights, transfer functions, architectures and learning rules. Experimental results have shown that the developed method can produce compact, efficient...
Automatically inferring drug gene regulatory networks models from microarray time series data is a challenging task. The ordinary differential equations models are sensible, but difficult to build. We extended our reverse engineering algorithm for gene networks (RODES), based on genetic programming, by adding a neural networks feedback linearization component. Thus, RODES automatically discovers the...
A Brain-Computer Interface (BCI) is an interface that directly analyzes brain activity to transform user intentions into commands. Many known techniques use the P300 event-related potential by extracting relevant features from the EEG signal and feeding those features into a classifier. In these approaches, feature extraction becomes the key point, and doing it by hand can be at the same time cumbersome...
The Polynomial Artificial Neural Network (PANN) has shown to be a powerful Network for time series forecasting. Moreover, the PANN has the advantage that it encodes the information about the nature of the time series in its architecture. However, the problem with this type of network is that the terms needed to be analyzed grow exponentially depending on the degree selected for the polynomial approximation...
In order to implement a model-based predictive control methodology for a research greenhouse several predictive models are required. This paper presents the modelling framework and results about the models that were identified. RBF neural networks are used as non-linear auto-regressive and non-linear auto-regressive with exogenous inputs models. The networks parameters are determined using the Levenberg-Marquardt...
A hybrid method combining artificial neural network (ANN) with genetic algorithm (GA) is discussed in this paper. A new strategy of optimization on ANN structure and weights based on multi-population GA is proposed, and the quantitative optimization of ANN is realized. The Levenberg-Marquardt(LM) algorithm is used for further training the neural network, which can avoid the weak local searching ability...
Job-shop scheduling problem is a typical representative of NP-complete problems and it is also a popular topic for the researchers during the recent decades. Lots of artificial intelligence techniques were used to solve this kind of problems, such as: genetic algorithm, tabu searching method, simulated annealing and neural network. Based on the previous research of Zhou and Willems, this paper proposes...
Face recognition system usually consists of components of feature extraction and pattern classification. However, not all of extracted facial features contribute to the classification phase positively because of the variations of illumination and poses in face images. In this paper, a three-step feature selection algorithm is proposed in which discrete cosine transform (DCT) and genetic algorithms...
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