September 2009
Systems Science;2009, Vol. 35 Issue 3, p55
Academic Journal
A model of the application of syntactic pattern recognition methods in a computer system supporting strategic management in an enterprise (based on Balanced Scorecard) is presented in the paper. The goal of BCSPRS system (Balanced ScoreCard Pattern Recognition System) is the analysis and recognition of patterns representing changes of values of strategic measures in time-series. The model of BCSPRS is based on the syntactic pattern recognition approach with the use of GDPLL(k) grammars (quasi contextsensitive string grammars). The model is efficient computationally and it can be used for the recognition of even very complex patterns. Additionally, the model provides a self-learning feature: the knowledge base about the patterns to be recognized can be automatically extended by the proper grammatical inference algorithms.


Related Articles

  • Parameters Tuning of an Immune Algorithm for Time-Series Pattern Recognition to Improve Ability to Escape from a Local Optimum and Achieve a Better Solution. PAPROCKA, Iwona; GWIAZDA, Aleksander; SKOŁUD, Bożena // Applied Mechanics & Materials;2015, Vol. 791, p342 

    In this paper, time-series pattern recognition is applied for a damage evaluation sequence represented by a frequency response change due to a damage coefficient occurrence. The objective of this paper is parameters tuning of pattern recognition system which discovers dependencies between data...

  • Preface of the Special Issue on Hybrid Intelligent Systems using Soft Computing Techniques. Castillo, Oscar; Melin, Patricia // Engineering Letters;2012, Vol. 20 Issue 1, p58 

    No abstract available.

  • PLA Data Reduction for Speeding Up Time Series Comparison. Boucheham, Bachir // International Arab Journal of Information Technology (IAJIT);Sep2012, Vol. 9 Issue 5, p459 

    We consider comparison of two Piecewise Linear Approximation (PLA) data reduction methods, a recursive PLA segmentation technique (Douglas-Peucker Algorithm) and a sequential PLA-segmentation technique (FAN) when applied in prior of our previously developed time series alignment technique SEA,...

  • Optimization Algorithm with Kernel PCA to Support Vector Machines for Time Series Prediction. Qisong Chen; Xiaowei Chen; Yun Wu // Journal of Computers;Mar2010, Vol. 5 Issue 3, p380 

    As an effective tool in pattern recognition and machine learning, support vector machine (SVM) has been adopted abroad. In developing a successful SVM classifier, eliminating noise and extracting feature are very important. This paper proposes the application of kernel Principal Component...

  • Sequential Pattern Mining for Uncertain Data Streams using Sequential Sketch. Jingyu Chen; Ping Chen // Journal of Networks;Feb2014, Vol. 9 Issue 2, p252 

    Uncertainty is inherent in data streams, and present new challenges to data streams mining. For continuous arriving and large size of data streams, modeling sequences of uncertain time series data streams require significantly more space. Therefore, it is important to construct compressed...

  • Generalized gradient learning on time series. Jain, Brijnesh // Machine Learning;Sep2015, Vol. 100 Issue 2/3, p587 

    The majority of machine learning algorithms assumes that objects are represented as vectors. But often the objects we want to learn on are more naturally represented by other data structures such as sequences and time series. For these representations many standard learning algorithms are...

  • Extend semi-supervised ELM and a frame work. Liu, ShengLan; Feng, Lin; Wang, HuiBing; Xiao, Yao // Neural Computing & Applications;Jan2016, Vol. 27 Issue 1, p205 

    Extreme learning machines (ELM) is a state-of-the-art classification algorithm. Many applications and ELM modified versions have been proposed in recent years. We propose a frame work of semi-supervised ELM (SELM) based on SELM (Liu et al. in Neurocomputing 74:2566-2572, ). In this paper, we...

  • Numerical Time-Series Pattern Extraction Based on Irregular Piecewise Aggregate Approximation and Gradient Specification. Ohsaki, Miho; Abe, Hidenao; Yamaguchi, Takahira // New Generation Computing;2007, Vol. 25 Issue 3, p213 

    his paper proposes and evaluates a method for extracting interesting patterns from numerical time-series data which takes account of user subjectivity. The proposed method conducts irregular sampling on the data preserving the subjectively noteworthy features using a user specified gradient. It...

  • CLASSIFICATION ALGORITHEM FOR TIME SERIES DATAMINING. Korrapati, Raghu B.; Katneni, Vedavathi; Klareti, Srinivasa Rao // Allied Academies International Conference: Proceedings of the Ac;Apr2010, Vol. 14 Issue 1, p37 

    Classification is an important problem in Pattern recognition. The classification of the data is based on the nature of the data collected according to spatial or temporal characteristics. In temporal data collection, time series forms an important class. Time series classification is one of the...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics