A Normalization Method Based on Rough Set Theory

Based on rough set and basic theory of data fusion, the data fusion algorithm combining rough set theory and BP neural network is studied. Since rough set theory can effectively simplify information, cut down the tagged dimension . This paper will be rough set theory and neural networks combined, using channel capacity of knowledge relative reduction algorithms to simplify the input information. Rough set theory is first used to process the sample data, and eliminate the redundant information, then reduce the scale of neural network, improve the identification rate, and improve the efficiency of the whole data fusion system. The effectiveness of the improved algorithm is demonstrated by an example compared with the traditional neural network system.

Ellipsoidal basis function(EBF) can make the partition and limitary of input space. Compared with the Guassian function of radial basis function(RBF) neural network, the EBF can make the partition of input space more specific, which has the higher capability of pattern recognition. However, the neural network has a common problem of training the weight and threshold. The evolution of genetic algorithm(GA) can maximumly optimize the training time of neural network. In this paper, a new method based on GA-EBF neural network was proposed. The simulation experiment shows that the proposed method has a higher rate of fault diagnosis than that of RBF neural network. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>

In the deregulated power systems, the available transfer capability (ATC) should be known for secure and reliable operation. ATC mainly depends on load for a particular transaction. Due to complex nature of load, it is better if the ATC estimator is able to handle this complex nature. This paper presents fully complex-valued radial basis function (FC-RBF) neural network approach for ATC estimation for bilateral transaction under normal condition. The training patterns for neural network are generated using differential evolution algorithm (DEA). The important feature of the proposed method is the use of input reduction techniques to improve the performance of the developed network. Differential evolution feature selection (DEFS) technique is proposed to reduce the complexity and training time of neural network. The proposed method is tested on IEEE 118 bus system, and results are compared with DEA and repeated power flow (RPF) results. The test results show that the proposed method reduces the training time and it is suitable for online application.

In this paper studies a kind of information matching method based on neural network, and the method combines first-order logic and semantic similarity to complete the table matching, using SOM+ to generate feedback neural network to complete the field matching. The method can effectively reduce the matching time complexity. And by using history match, it effectively reduces the training time of neural network, improving the accuracy of matching.

The safe reliability assessment of coal mine safety system is a multifactor overall merit problem. In this paper, we firstly introduced the principle and method of coal mine safety assessment based on rough set - neural network - evidence theory. Secondly, we used rough set method to carry out the pretreatment of information. Thirdly, according to the information structure after treatment, we formed the information processing system of rough set - neural network - evidence theory. Finally, we carried out the analysis of fusion result. In conclusion, we applied rough set, neural network, D-S evidence theory to the processing of safe mass monitoring data, which can reduce the uncertainty in the process of data processing. It provides us an effective basis and method to know the security situation and the development trend, and to analyze the cause and the harm of the coal mine accident.

Meteorological conditions such as air density, temperature, solar radiation etc. strongly affect the power generation from solar, and thus, the prediction and estimation process should consider weather conditions as critical inputs. The nature of weather forecast is highly unpredictable, so many applications use meteorological data from in-place on-site sensors to add to the forecast and some use complex networks with complicated mapping. The in-situ sensor approach and dense mapping methods, however, present several drawbacks. First, the use of sensors give rise to extra operational, installation and maintenance cost. Second, it requires significant amount of time to capture and accumulate data for various occasions and scenarios, and in addition, sensor itself can be the cause of error measurements. The complex methods are computational inefficient and may present suboptimal convergence. This paper presents a sensorless solar output power forecasting based on historical weather (publicly available from met office) and PV data. The algorithm uses simple to implement neural networks with few neurons and hidden layers for its training and allows for day a head forecast. The proposed methodology presents a guideline on how to select the relevant data from weather and how it affects the accuracy and training time of neural network. The benefit of developed method is an improvement on the energy management, utilization and reliability of the microgrid.

Soft rough fuzzy sets and soft fuzzy rough sets

Rough set theory for the interval-valued fuzzy information systems

Theories of fuzzy sets and rough sets are powerful mathematical tools for modelling various types of uncertainty. Dubois and Prade investigated the problem of combining fuzzy sets with rough sets. Soft set theory was proposed by Molodtsov as a general framework for reasoning about vague concepts. The present paper is devoted to a possible fusion of these distinct but closely related soft computing approaches. Based on a Pawlak approximation space, the approximation of a soft set is proposed to obtain a hybrid model called rough soft sets. Alternatively, a soft set instead of an equivalence relation can be used to granulate the universe. This leads to a deviation of Pawlak approximation space called a soft approximation space, in which soft rough approximations and soft rough sets can be introduced accordingly. Furthermore, we also consider approximation of a fuzzy set in a soft approximation space, and initiate a concept called soft---rough fuzzy sets, which extends Dubois and Prade's rough fuzzy sets. Further research will be needed to establish whether the notions put forth in this paper may lead to a fruitful theory.

Considering training time of traditional BP neural network is too long and it can not solve the problem that input vector is multiple-valued, a new method based on rough BP neural network for fault diagnosis is presented. The approach is realized by applying PSO (particle swarm optimization) to discretize continuous attributes, using property of dependency of rough set to carry through attribute reduction and designing a kind of rough BP neural network according to the optimal decision system for fault diagnosis. A practical example is given to show the method is feasible and available.

Theories of fuzzy sets and rough sets are powerful mathematical tools for modelling various types of uncertainty. Molodtsov (Comput Math Appl 37:19–31, 1999 [6]) initiated a novel concept called soft sets, a new mathematical tool for dealing with uncertainties. It has been found that fuzzy sets, rough sets, and soft sets are closely related concepts (Aktas and Cagman in Inf Sci 1(77):2726–2735, 2007 [1]). Research works on soft sets are very active and progressing rapidly in these years. In 2001, Maji et al. (J Fuzzy Math 9(3):589–602, 2001 [5]) proposed the idea of intuitionistic fuzzy soft set theory and established some results on them. Based on an equivalence relation on the universe of discourse, Dubois and Prade (Int J Gen Syst 17:191–209, 1990 [3]) introduced the lower and upper approximation of fuzzy sets in a Pawlak approximation space and obtained a new notion called rough fuzzy sets. Feng et al. (Soft Compt 14:899–911, 2009 [4]) introduced lower and upper soft rough approximation of fuzzy sets in a soft approximation space and obtained a new hybrid model called soft rough fuzzy sets which is the extension of Dubois and Prade’s rough fuzzy sets. The aim of this chapter is to consider lower and upper soft rough intuitionistic fuzzy approximation of intuitionistic fuzzy sets in intuitionistic fuzzy soft approximation space (IF soft approximation space) and obtain a new hybrid model called soft rough intuitionistic fuzzy sets which can be seen as extension of both the previous work by Dubois and Prade and Feng et al.

The work is devoted to resolving the contradiction between the accuracy of modelling nonlinear dynamics and the speed of model construction under conditions of limited computing resources. The purposeof the work is to reduce the time for building time delay neural networks while ensuring a given accuracy in the tasks of identifying nonlinear dynamic objects with continuous characteristics. This goal is achieved by developing a method for pre-training neural networks that reflect the basic characteristics of the subject area. The scientific novelty of the work is the development of a method for identifying nonlinear dynamic objectsin the form of time delay neural networks based on the use of a set of basic pre-trained neural networks that reflect the typical properties of the subject area. In contrast to the traditional approach to pre-training, the developed method allows building models of lower complexity.A formal criterion is proposed for determining the moment of termination of the neural network pre-training, the use of which allows avoiding retraining of the base model and ensuring a significant reduction in the model training time on the target data set. The practical utility of the work lies in the development of an algorithm for the method of pre-training time delay neural networks in the tasks of identifying nonlinear dynamic objects with continuous characteristics, which allows to significantlyreduce the training time of neural networks without losing model accuracy. The value of this study is todetermine the area of effective use of the proposed method, namely, when the general and target datasets do not have significant differences and the target dataset is of sufficient size to reflect the properties of the research object.

The main challenge in using supervised neural networks in data mining applications is to get explicit knowledge from these models. For this purpose, a study on knowledge acquisition from supervised neural networks employed for classification problems is presented. An algorithm for rule extraction from neural networks, based on the RX algorithm is developed. This algorithm, named modified RX, is experimentally evaluated in two different domains: Iris Plants Database and Pima Indians Diabetes Database. The results are compared to those obtained by classification trees. As far as the efficacy is concerned, one observes that the successful application of the algorithm mainly depends on the knowledge representation acquired by the connectionist model, whereas the efficiency only depends on the neural network training time.

As for the problem of too long training time of convolution neural network (CNN), this paper proposes a fast training method for CNN in SAR automatic target recognition (ATR). The CNN is divided into two parts: one that contains all the convolution layers and sub-sampling layers is considered as convolutional auto-encoder (CAE) for unsupervised training to extract high-level features; the other that contains fully connected layers is regarded as shallow neural network (SNN) to work as a classifier. The experiment based on MSATR database shows that the proposed method can tremendously reduce the training time with little loss of recognition rate.

With the requirements of the power load predicting accuracy becoming higher and higher, and the actual power load is also a complex nonlinear system limited by various uncertain factors. Given only considering the influence of a single model on the load forecasting, the predicting accuracy is absolutely not high under the comprehensive effects of multiple factors. Ensuring to make the electrical model satisfy the requirements, there is access to improving the accuracy of load predicting results through analysis of the relationship between load and multiple factors in power load forecasting. This paper combines the rough set theory with information fusion, firstly a decision table of attributes is established on the basis of historical datum, for the purpose of simplifying the attribute index in mining to obtain important information. Following step is to rank the importance of the factors influencing the load according to the degree of effect, with grouping these sorted factors into attributes combination and using the same kind of load forecasting method to predict each combination's own results, and finally all groups of the predicting results are integrated by fusion, where then final predicting result can be obtained. The results of the algorithmic example show that groups of predicting results integrated based on information fusion theory are better than the results got through any casual single attribute combination. And fusion results are a combined action of a variety of related factors, enhancing the favorable trend, where at the same time it has reduced the uncertainty brought by multiple factor comprehensive effect. Based on information fusion the predicting results are helpful to reduce the prediction error, which is feasible and effective. With the explosive growth on the amount of in today's information age, the wave of data is coming, fast and furious. The era of big proposes a new challenge for the development in electricity industry. With the advance of electricity industry, intelligence, automation and interaction will bring many datum casing electrical big generation. Due to the numerous index of power load forecasting, different goals of all kinds of power load forecasting, even with the same kind of theory, the forecast model to predict no matter from the form, content or effect also vary widely. The load in the medium and long-term prediction of power system is greatly influenced by economy, society and meteorology factors and other uncertainties, etc. How to extract the key factor from multiple factors and reduce the results' uncertainty arising from the comprehensive effect of multiple factors, and improving the precision has been becoming a serious problem to be solved. Aiming at these problems, the paper based on the literature (8) proposing medium-and long- term load forecasting based on partial least squares regression analysis, an more optimized and advanced method is put forward to improve accuracy of prediction. The rough set theory and information fusion are introduced into the model, which respectively advance pre-processing and optimize the integration of multiple sets of results after prediction, in order to reduce uncertainty caused by varieties factors affecting and improve accuracy of forecasting results. II. THE ROUGH SET THEORY In the era of big data, on account of the characteristics on grid volume, which is large amount, variety type, and more speed, the traditional attribute simplified methods have been unable to complete the pre-processing on electrical big data. To this end the paper proposes a method on power big simplified processing based on cloud computing technology. The rough set theory which offers a new way for mining, is a new mathematical tool to deal with uncertain and imprecise problems. In this paper, rough sets method is used in power load forecasting to analyze the dependence of load on each factor and the importance of various properties in condition attribute set. In the decision system R={U, A}, the rough set divides A(attribute) into two categories C and D, where C is condition attribute, D is decision attribute, and U is domain. The ternary collection R={U,C,D} is known as a decision-making system. An important application of rough set in the analysis is to find the dependence among attributes from a known decision system R={U,C,D}. The dependency is defined as follows.