PCA-Based Elman Neural Network Algorithm

Recently, anomaly detection has acquired a realistic response from data mining scientists as a graph of its reputation has increased smoothly in various practical domains like product marketing, fraud detection, medical diagnosis, fault detection and so many other fields. High dimensional data subjected to outlier detection poses exceptional challenges for data mining experts and it is because of natural problems of the curse of dimensionality and resemblance of distant and adjoining points. Traditional algorithms and techniques were experimented on full feature space regarding outlier detection. Customary methodologies concentrate largely on low dimensional data and hence show ineffectiveness while discovering anomalies in a data set comprised of a high number of dimensions. It becomes a very difficult and tiresome job to dig out anomalies present in high dimensional data set when all subsets of projections need to be explored. All data points in high dimensional data behave like similar observations because of its intrinsic feature i.e., the distance between observations approaches to zero as the number of dimensions extends towards infinity. This research work proposes a novel technique that explores deviation among all data points and embeds its findings inside well established density-based techniques. This is a state of art technique as it gives a new breadth of research towards resolving inherent problems of high dimensional data where outliers reside within clusters having different densities. A high dimensional dataset from UCI Machine Learning Repository is chosen to test the proposed technique and then its results are compared with that of density-based techniques to evaluate its efficiency.

Pavement distress images are typical high dimensional nonlinear data. Manifold learning algorithms can find the intrinsic characteristic hidden in the distress images, which helps to better recognize them. Unlike most of manifold learning algorithms, multilayer autoencoders have solved the data reconstructed problem through building a bi-directional mapping between the high dimensional data and the low dimensional data. An automatic pavement distress image recognition method based on multilayer autoencoders was proposed, which combined the image processing method and multilayer autoencoders. In the method, the distress images were firstly processed with the image processing method. Then the images were reduced dimensions and reconstructed with multilayer autoencoders. Lastly, the distress type was recognized through the network. Experiments showed that the recognition accuracy with the proposed method was great higher than that with the BP neural network.

Conventional Vector Autoregressive (VAR) modelling methods applied to high dimensional neural time series data result in noisy solutions that are dense or have a large number of spurious coefficients. This reduces the speed and accuracy of auxiliary computations downstream and inflates the time required to compute functional connectivity networks by a factor that is at least inversely proportional to the true network density. As these noisy solutions have distorted coefficients, thresholding them as per some criterion, statistical or otherwise, does not alleviate the problem. Thus obtaining a sparse representation of such data is important since it provides an efficient representation of the data and facilitates its further analysis. We propose a fast Sparse Vector Autoregressive Greedy Search (SVARGS) method that works well for high dimensional data, even when the number of time points is relatively low, by incorporating only statistically significant coefficients. In numerical experiments, our methods show high accuracy in recovering the true sparse model. The relative absence of spurious coefficients permits accurate, stable and fast evaluation of derived quantities such as power spectrum, coherence and Granger causality. Consequently, sparse functional connectivity networks can be computed, in a reasonable time, from data comprising tens of thousands of channels/voxels. This enables a much higher resolution analysis of functional connectivity patterns and community structures in such large networks than is possible using existing time series methods. We apply our method to EEG data where computed network measures and community structures are used to distinguish emotional states as well as to ADHD fMRI data where it is used to distinguish children with ADHD from typically developing children.

BackgroundIdentifying variants associated with complex human traits in high-dimensional data is a central goal of genome-wide association studies. However, complicated etiologies such as gene-gene interactions are ignored by the univariate analysis usually applied in these studies. Random Forests (RF) are a popular data-mining technique that can accommodate a large number of predictor variables and allow for complex models with interactions. RF analysis produces measures of variable importance that can be used to rank the predictor variables. Thus, single nucleotide polymorphism (SNP) analysis using RFs is gaining popularity as a potential filter approach that considers interactions in high-dimensional data. However, the impact of data dimensionality on the power of RF to identify interactions has not been thoroughly explored. We investigate the ability of rankings from variable importance measures to detect gene-gene interaction effects and their potential effectiveness as filters compared to p-values from univariate logistic regression, particularly as the data becomes increasingly high-dimensional.ResultsRF effectively identifies interactions in low dimensional data. As the total number of predictor variables increases, probability of detection declines more rapidly for interacting SNPs than for non-interacting SNPs, indicating that in high-dimensional data the RF variable importance measures are capturing marginal effects rather than capturing the effects of interactions.ConclusionsWhile RF remains a promising data-mining technique that extends univariate methods to condition on multiple variables simultaneously, RF variable importance measures fail to detect interaction effects in high-dimensional data in the absence of a strong marginal component, and therefore may not be useful as a filter technique that allows for interaction effects in genome-wide data.

In recent year, stock investor in Indonesia increased rapidly, so it is required to do analysis about the stock that helps the investor in their investment plan. Clustering is beneficial to select the appropriate stock for investors. Unfortunately, stock prices keep varying from time to time. Consequently, it is not an easy work to select the stock for investment. In addition, stock price time series data are high dimensional data that influenced by many factors. In this study, high dimensional data are obtained by the time frame of each factor. Therefore, it is important to use a suitable technique to cluster high dimensional data. This paper presents High Dimensional Data Clustering (HDDC), a model-based clustering based on Gaussian Mixture Model, using the Expectation-Maximization (EM) algorithm. HDDC via EM algorithm gives a more robust result, and it possible to make an additional assumption. Moreover, this paper combines a high-dimensional clustering technique HDDC via EM algorithm and the most popular feature extraction technique Principal Component Analysis (PCA). This paper comparing methods of clustering technique HDDC and the combination between HDDC and PCA to know the most effective method which gives better result in clustering high-dimensional time series data. The 155 data features are reduced to 7 principal components using PCA analysis. Despite PCA has increased the time efficiency of building the model, clustering technique HDDC via EM algorithm enables to handle the high-dimensional data better than the combination with PCA.

Accurate estimation of the state of charge (SOC) of lithium batteries is the research hotspot. As a dynamic recurrent neural network, Elman neural network has a simple structure and can adapt to time-varying characteristics, which have led to its widespread application in the field of SOC prediction. However, the simple network structure can also cause problems such as low learning efficiency, easy to fall into local extremes, and difficulty converge to the optimal weight solution. This paper proposes to combine the whale optimization algorithm (WOA) with genetic algorithm (GA) to optimize the weights and thresholds of the Elman neural network, thus improving the learning speed and prediction accuracy. We select three parameters of current, voltage and temperature as system variables, and simulate the experimental data. The experimental results show that, compared with the traditional Elman neural network and the Elman neural network optimized only by genetic algorithm, the hybrid algorithm performs best. The number of network iterations is small, and the average error drops below 1%.

Advances in instrumentation technology have equipped us with better process controlling set-ups for error detection and control that occurs in the industrial process plants. This in turn generates a large amount of data that is not always information rich. Additional sensor like a soft sensor can be used to modify the sensor to generate information rich data. Soft sensor are computational models that aid in the continuous or partial estimation of some process parameters which otherwise are difficult to measure using hardware sensors. In this paper, a soft sensor is modelled for a waste water treatment plant where principal component analysis is used to reduce the high dimensional data that is available from the hardware sensor to a low dimensional information rich data and also to identify the type of measured data. This can be used in conjunction with the sensor in the plant to improve the control performance and yield better product quality. Keywords: WWTP, soft sensors, principal component analysis, latent vectors, scores Cite this Article Barasha Mali, S. H. Laskar. Soft Sensor for Estimation and Identification of Reduced Dimensional Quality Control Inputs. Journal of Instrumentation Technology & Innovations . 2017; 7(3): 24–29p.

High-dimensional large sample data sets, between feature variables and between samples, may cause some correlative or repetitive factors, occupy lots of storage space, and consume much computing time. Using the Elman neural network to deal with them, too many inputs will influence the operating efficiency and recognition accuracy; too many simultaneous training samples, as well as being not able to get precise neural network model, also restrict the recognition accuracy. Aiming at these series of problems, we introduce the partial least squares (PLS) and cluster analysis (CA) into Elman neural network algorithm, by the PLS for dimension reduction which can eliminate the correlative and repetitive factors of the features. Using CA eliminates the correlative and repetitive factors of the sample. If some subclass becomes small sample, with high-dimensional feature and fewer numbers, PLS shows a unique advantage. Each subclass is regarded as one training sample to train the different precise neural network models. Then simulation samples are discriminated and classified into different subclasses, using the corresponding neural network to recognize it. An optimized Elman neural network classification algorithm based on PLS and CA (PLS-CA-Elman algorithm) is established. The new algorithm aims at improving the operating efficiency and recognition accuracy. By the case analysis, the new algorithm has unique superiority, worthy of further promotion.

There is a function of dynamic mapping when processing non-linear complex data with Elman neural networks. Because Elman neural network inherits the feature of back-propagation neural network to some extent, it has many defects; for example, it is easy to fall into local minimum, the fixed learning rate, the uncertain number of hidden layer neuron and so on. It affects the processing accuracy. So we optimize the weights, thresholds and numbers of hidden layer neurons of Elman networks by genetic algorithm. It improves training speed and generalization ability of Elman neural networks to get the optimal algorithm model. It has been proved by instance analysis that new algorithm was superior to the traditional model in terms of convergence rate, predicted value error, number of trainings conducted successfully, etc. It indicates the effect of the new algorithm and deserves further popularization.

How to make full use of high-dimensional massive power data to improve the accuracy and efficiency of short-term electrical load forecasting is a challenging problem to be solved. This paper discusses a parallel load forecasting technique with an Elman Neural Network(ENN) based on the MapReduce(MR) programming model. Specially, a Coarse-Grained Parallel Genetic Algorithm(CPGA) is introduced into the process of ENN training for obtaining the promising weights and thresholds. In the framework of MR, a parallel load-forecasting model of MR- CPGA-ENN is established. Case study with different scenario- based electrical load data sets and meteorological information confirm that both the accuracy and efficiency of the developed model are superior to the conventional models compared.

Background/Objectives: This paper aims to develop a novel intelligent recommender system suitable for high dimensional data where multiple factor variables influence on multiple response variables. Methods/Statistical Analysis: This paper suggests that the structure of the cause-and-effect relations among the variables can be represented in a simple form called structured association network model (SANM). Based on the SANM and conventional data mining techniques such as association rule mining and naive Bayesian classifier, the proposed recommender system computes three novel recommendation scores for each response variables, and the variables with high scores can be selected for recommendation. Findings: For illustration, the proposed recommender system has been applied to a mass health examination result data set. Owing to its simple structure, a SANM for a given data set can be easily obtained by simply identifying the factor and the response variables, and the experiment results revealed that the proposed system can identify the recommendable items for the individuals more effectively than the traditional classification techniques such as naive Bayesian classifier. Consequently, we can conclude that the proposed recommender system can deal with high dimensional transaction data set in more effective manner than the traditional approaches where the underlying semantic relationships among the variables are not considered. Applications/Improvements: The proposed recommender system is useful for evaluation of the potential risks of specific diseases. Moreover, the recommendation scores can also be used as a tool for feature construction.

Fuzzy partition based soft subspace clustering and its applications in high dimensional data

High dimensional data clustering is a difficult task due to the curse of dimensionality. Traditional clustering methods usually fail to produce meaningful results for high dimensional data. Hypergraph partition is believed to be a promising method for dealing with this challenge. In this work, a new high dimensional clustering method called Merging Dense SubGraphs (MDSG) is proposed. A graph G is first constructed from the data by defining an adjacency relationship between the data points using Shared k Nearest Neighbors (SNN). Then a hypergraph is created from the graph G by defining the hyperedges to be all the maximal cliques in the graph. After the hypergraph is produced, an improved hypergraph partitioning method is used to produce the final clustering results. The proposed MDSG method is evaluated on several real high dimensional handwritten datasets, and the experimental results show that the proposed method is superior to the traditional clustering method and other hypergraph partition methods for high dimensional handwritten data clustering.

Outlier detection aims to find a data sample that is significantly different from other data samples. Various outlier detection methods have been proposed and have been shown to be able to detect anomalies in many practical problems. However, in high dimensional data, conventional outlier detection methods often behave unexpectedly due to a phenomenon called the curse of dimensionality. In this paper, we compare and analyze outlier detection performance in various experimental settings, focusing on text data with dimensions typically in the tens of thousands. Experimental setups were simulated to compare the performance of outlier detection methods in unsupervised versus semi-supervised mode and uni-modal versus multi-modal data distributions. The performance of outlier detection methods based on dimension reduction is compared, and a discussion on using k-NN distance in high dimensional data is also provided. Analysis through experimental comparison in various environments can provide insights into the application of outlier detection methods in high dimensional data.

Various machine learning, cloud computing and big data models are most utilized control parameters in software applications. Therefore these systems are most demand at current trending decades. But these models are needs very low data access time, speed for process. Day to day life data storage servers and devices are costly and hardware complex, so dimensionality is increases with rapid manner. Any type of optimization techniques access takes more time consumption for high dimensional data. So, more applications related problems are occurs only at high dimensional data space does not at low space dimensional data. In this research proposes a dimensional reduction technique with Logistic regression (LR) model. This LR model is most helpful for dimensional reduction and clustering problems. LRML method has reduced the dimensional data size and achieved thee efficiency by 95.3% and ratio of reduction by 35.76%.