Network-based approach to online cursive script recognition

The idea of combining the network of HMMs and the dynamic programming-based search is highly relevant to online handwriting recognition. The word model of HMM network can be systematically constructed by concatenating letter and ligature HMM's while sharing common ones. Character recognition in such a network can be defined as the task of best aligning a given input sequence to the best path in the network. One distinguishing feature of the approach is that letter segmentation is obtained simultaneously with recognition but no extra computation is required.     

A systemic approach to process modeling

This article presents a system's view of a common sense management model for systems (COSMOS) [1]. Salient features of COSMOS are introduced through the unfolding story of process development of a hypothetical corporation called IM Co. This systemic view models the dynamic complexity of a system or organization so that inerrelationships, rather than things, patterns of changes, rather than snapshots, are captured. COSMOS views changes as an ongoing opportunity and provides guidance for system changes to be performed in small steps. However, these small steps can build a long lever that is capable of producing dramatic effects. When performing changes, essential trade-offs have to be considered. COSMOS provides three perspectives—activity, communication, and infrastructure—of a process to assist managers in dealing with these trade-offs. The model also includes a generic two-level hierarchy—control and execution levels—to keep balance among the three perspectives.Small Is Beautiful — Ernst Fredrich Schumacher Give me a lever long enough ... and single-handed I can move the world — Archimedes     

A new approach to fuzzy modeling

This paper proposes a new approach to fuzzy modeling. The suggested fuzzy model can express a given unknown system with a few fuzzy rules as well as Takagi and Sugeno's model (1985), because it has the same structure as that of Takagi and Sugeno's model. It is also as easy to implement as Sugeno and Yasukawa's model (1993) because its identification mimics the simple identification procedure of Sugeno and Yasukawa's model. The suggested algorithm is composed of two steps: coarse tuning and fine tuning. In coarse tuning, fuzzy C-regression model (FCRM) clustering is used, which is a modified version of fuzzy C-means (FCM). In fine tuning, gradient descent algorithm is used to precisely adjust parameters of the fuzzy model instead of nonlinear optimization methods used in other models. Finally, some examples are given to demonstrate the validity of this algorithm     

A simple approach to system modeling

This paper describes an approach to system modeling based on heuristic mean value analysis. The virtues of the approach are conceptual simplicity and computational efficiency. The approach can be applied to a large variety of systems, and can handle features such as resource constraints, tightly and loosely coupled multiprocessors, distributed processing, and certain types of CPU priorities. Extensive validation results are presented, including truly predictive situations. The paper is intended primarily as a tutorial on the method and its applications, rather than as an exposition of research results.     

A simple approach to system modeling

This paper describes an approach to system modeling based on heuristic mean value analysis. The virtues of the approach are conceptual simplicity and computational efficiency. The approach can be applied to a large variety of systems, and can handle features such as resource constraints, tightly and loosely coupled multiprocessors, distributed processing, and certain types of CPU priorities. Extensive validation results are presented, including truly predictive situations. The paper is intended primarily as a tutorial on the method and its applications, rather than as an exposition of research results.     

A formal approach to fuzzy modeling

A formalism for coding fuzzy models of dynamical systems is presented. It is shown that the formalism is rich enough to capture the performance of arbitrary conventional discrete time dynamical systems whose transition maps are polynomials with rational coefficients. The proof of this fact provides a constructive algorithm for generating fuzzy models to arbitrarily closely approximate an arbitrary map on a compact set. Our modeling formalism highlights the similarities between fuzzy systems and hybrid control systems. We hope to be able to exploit these similarities by extending results from the area of hybrid systems to the fuzzy domain and vice versa     

A knowledge-based approach to CIM modeling

This paper discusses issues in building a conceptual modeling approach that has sufficient semantic power to represent the complexities of decision making in CIM systems. To express the information in a conceptual model we make use of an amalgam of powerful paradigms: a hybrid methodology, that integrates the concepts of object-oriented programming, messagepassing semantics, and temporal logic. We further illustrate an example incorporating some of these concepts.     

A new approach to fuzzy-neural system modeling

We develop simple but effective fuzzy-rule based models of complex systems from input-output data. We introduce a simple fuzzy-neural network for modeling systems, and we prove that it can represent any continuous function over a compact set. We introduce “fuzzy curves” and use them to: 1) identify significant input variables, 2) determine model structure, and 3) set the initial weights in the fuzzy-neural network model. Our method for input identification is computationally simple and, since we determine the proper network structure and initial weights in advance, we can train the network rapidly. Viewing the network as a fuzzy model gives insight into the real system, and it provides a method to simplify the neural network     

A dempster-shafer approach to modeling agent preferences for plan recognition

Plan recognition is an important task whenever a system has to take into account an agent's actions and goals in order to be able to react adequately. Most plan recognizers work by merely maintaining a set of equally plausible plan hypotheses each of which proved compatible with recent observations without taking into account individual preferences of the currently observed agent. Such additional information provides a basis for ranking the hypotheses so that the best one can be selected whenever the system is forced to react (e.g., to provide help to the user of a software system to accomplish his goals). Furthermore, hypotheses with low valuations can be excluded from considerations at an early stage. In this paper, an approach to the quantitative modeling of the required agent-related data and their use in plan recognition is presented. It relies on the DempsterShafer Theory and provides mechanisms for the initialization and update of corresponding numerical values.     

A behavioral approach to linear exact modeling

The behavioral approach to system theory provides a parameter-free framework for the study of the general problem of linear exact modeling and recursive modeling. The authors present the solution of the (continuous-time) polynomial-exponential time series modeling problem. Both recursive and nonrecursive solutions are provided and classified according to properties like complexity and controllability. It is shown, in particular, that recursive modeling corresponds to updating by means of a cascade inter-connection of systems. As a special case, the solution of several other problems, such as rational interpolation, realization, and modeling of arbitrary discrete-time time series, is obtained     

A hierarchical/relational approach to modeling

We describe a hierarchical/relational approach to math programming modeling. The approach transforms free-form generic modeling constructs into linear and nonlinear models which are independent of end-user data structures. The underlying relationships with graph-based interfaces and the inherent aggregation/disaggregation capabilities of the approach are also discussed.The modeling approach will be illustrated with several process industry applications including distribution planning, operations planning, and production scheduling.     

A transformed input-domain approach to fuzzy modeling

This paper presents an explanation of a fuzzy model considering the correlation among components of input data. Generally, fuzzy models have a capability of dividing an input space into several subspaces compared to a linear model. But hitherto suggested fuzzy modeling algorithms have not taken into consideration the correlation among components of sample data and have addressed them independently, which results in an ineffective partition of the input space. In order to solve this problem, this paper proposes a new fuzzy modeling algorithm, which partitions the input space more effectively than conventional fuzzy modeling algorithms by taking into consideration the correlation among components of sample data. As a way to use the correlation and divide the input space, the method of principal component is used. Finally, the results of the computer simulation are given to demonstrate the validity of this algorithm     

A computational approach to modeling commodity markets

We build an agent based computational framework to study large commodity markets. A detailed representation of the consumers, producers and the market is used to study the micro level behavior of the market and its participants. The user can control players’ preferences, their strategies, assumptions of the model, its initial conditions, market elements and trading mechanisms. The first part of the paper describes the computational framework and its three main modules. The later part describes a case study that examines the decentralized market in detail, specifically the computational options available for matching the buyers and suppliers in a synthetic market. The study illustrates the sensitivity of the outcome of various economic variables, such as clearing price, quantity, profits and social welfare, to different matching schemes in a bilateral computational setting. Based on seven different matching orders for the buyers and suppliers, our study shows that the results can vary dramatically for different pairing orders.     

A SVM-based cursive character recognizer

This paper presents a cursive character recognizer, a crucial module in any cursive word recognition system based on a segmentation and recognition approach. The character classification is achieved by using support vector machines(SVMs) and a neural gas. The neural gas is used to verify whether lower and upper case version of a certain letter can be joined in a single class or not. Once this is done for every letter, the character recognition is performed by SVMs. A database of 57 293 characters was used to train and test the cursive character recognizer. SVMs compare notably better, in terms of recognition rates, with popular neural classifiers, such as learning vector quantization and multi-layer-perceptron. SVM recognition rate is among the highest presented in the literature for cursive character recognition.     

A contribution to a multimodal approach to knowledge modeling

At present a steep increase in efforts by human beings in search of information take place. Pattern Recognition extracts information from the real world. It is a tool for modelling and real-world learning. Thus it requires precise knowledge of the objectives to be achieved with the information it produces. Human senses are part of their interface with the environment. Our senses are signal encoders according some representation that is understood by the central nervous system. Multimodality in signal capturing makes the spectrum of activities of intelligent agents wider. This paper aims to stimulate research for providing multimodal information, say, information that can be captured by different senses. This technique can improve decision-making; promote the inclusion of sense disabled individuals, giving higher amplitude for usefulness of Pattern Recognition. It considers multimodal display of knowledge to practical feasibility of knowledge presentation adapted to human sensing and perception towards decision improvement and inclusion of perception-impaired people.