34068607
Self-adaptive neuro-fuzzy systems with fast parameter learning for autonomous underwater vehicle control
Presents a systematic approach for developing a concise self-adaptive neuro-fuzzy inference system (SANFIS) with a fast hybrid parameter learning algorithm for online learning the control knowledge for autonomous underwater vehicles (AUV). The multi-layered structure of SANFIS incorporates fuzzy basis functions for better function approximations. Based on the need of different applications, we investigate three SANFIS structures with three different types of fuzzy IF-THEN-rule-based models and cast the rule formation problem as a clustering problem. A recursive least squares algorithm and a modified Levenberg-Marquardt algorithm with limited memory are exploited to accelerate the learning process. Thus, incorporating an online clustering technique, a fast hybrid learning procedure and rule examination, the SANFIS is capable of self-organizing and self-adapting its internal structure for learning the required control knowledge for an AUV to follow desired trajectories. Computer simulations for modeling a control system for an AUV have been conducted to validate the effectiveness of the proposed SANFIS.
863373
A validity-guided support vector clustering algorithm for identification of optimal cluster configuration
This paper presents a validity-guided support vector clustering (SVC) algorithm for identifying an optimal cluster configuration. Since the SVC is a kernel based clustering approach, the parameter of kernel functions plays a crucial role in the clustering result. Without a priori knowledge of data sets, a validity measure, based on a ratio of overall cluster compactness to separation, has been developed to automatically determine a suitable parameter of the kernel functions. Using this parameter, the SVC algorithm is capable of identifying the optimal cluster number with compact and smooth arbitrary-shaped cluster boundaries. Computer simulations have been conducted to demonstrate the effectiveness of the proposed validity-guided SVC algorithm.
2659130
Self-adaptive recurrent neuro-fuzzy control for an autonomous underwater vehicle
This paper presents the utilization of a self-adaptive recurrent neuro-fuzzy control as a feedforward controller and a proportional-plus-derivative (PD) control as a feedback controller for controlling an autonomous underwater vehicle (AUV) in an unstructured environment. Without a priori knowledge, the recurrent neuro-fuzzy system is first trained to model the inverse dynamics of the AUV and then it utilized as a feedforward controller to compute the nominal torque of the AUV along a desired trajectory. The PD feedback controller computes the error torque to minimize the system error along the desired trajectory. This error torque also provides an error signal for online updating the parameters in the recurrent neuro fuzzy control to adapt in a changing environment. A systematic self-adaptive learning algorithm, consisting of a mapping-constrained agglomerative clustering algorithm for the structure learning and a recursive recurrent learning algorithm for the parameter learning, was developed to construct the recurrent neuro-fuzzy system to model the inverse dynamics of an AUV with fast learning convergence. Computer simulations of the proposed recurrent neuro-fuzzy control scheme and its performance comparison with an adaptive controller were conducted to validate the effectiveness of the proposed approach.
207558887
Self-adaptive neuro-fuzzy systems for autonomous underwater vehicle control
This paper presents a systematic approach for developing a concise self-adaptive neurofuzzy inference system (SANFIS) with a fast hybrid parameter learning algorithm for on-line learning of the control knowledge for autonomous underwater vehicle (AUV) control. The multi-layered network structure of SANFIS incorporates fuzzy basis functions for better function approximations. We investigate three SANFIS structures with three different types of fuzzy IF-THEN rule-based models and cast the rule formation problem as a clustering problem. A recursive least-squares algorithm and a modified Levenberg-Marquardt algorithm with limited memory are exploited to accelerate the parameter learning process. This hybrid parameter learning algorithm together with an on-line clustering technique and rule examination provide SANFIS with the capability of selforganizing and self-adapting its internal structure (i.e. the fuzzy rules and term sets) for learning the required control knowledge for an AUV to follow desired trajectories. Computer simulations for modeling a control system for an AUV have been conducted to validate the effectiveness of the proposed SANFIS.
206541019
An online self-organizing neuro-fuzzy control for autonomous underwater vehicles
Controlling autonomous underwater vehicles (AUVs) in an uncertain and unstructured environment presents many challenging control problems. Model-based control strategies have been used with limited success. The paper presents an online self-organizing neuro-fuzzy control that serves as a better alternative control scheme in controlling AUVs. The proposed self-organizing neuro-fuzzy controller is a six-layer feedforward neural network that is capable of self-constructing and self-restructuring its internal node connectivity and learning the parameters of each node based on incoming training data. Computer simulations have been conducted to validate the performance of the proposed neuro-fuzzy controller and an experimental verification has been scheduled to verify if on ODIN, an autonomous underwater vehicle developed at the University of Hawaii.
21940463
A hybrid method for unconstrained handwritten numeral recognition by combining structural and neural "gas" classifiers
Abstract This paper presents a hybrid method for handwritten numeral recognition that combines two compensatory recognition algorithms by analysing their performance for several aspects. The skeleton-based structural recognition algorithm employed in this method is robust under distortion but sensitive to noise and flaws. On the other hand, the neural network classifier, which uses scaled binary images as features and the neural “gas” model for classification, is relatively immune to noise and flaws but sensitive to distortion. The different performances of the two algorithms for broken, connected or slanted numerals, and the measurement-level decision provided by the neural network are detected and combined with different strategies to develop matching rules for each recognition method. Five combination methods based on performance analysis are developed to meet different requirements. As the two algorithms have fairly compensatory properties, the proposed method improves the recognition rate and reliability by exploiting the advantages and avoiding the weaknesses of each classifier. The experimental results from a large set of data show the efficiency and robustness of the proposed method.
52854920
Integrated architectural modeling and analysis for high-assurance command and control system design
A Real-Time Architectural Specification (RAS) approach and its application to command and control (C2) systems are presented. Our objective is to establish a formal foundation that will enable us to integrate existing rich but fragmented formal techniques for system specification and verification into a practical and scaleable formal engineering method to support the design and development of highly reliable real-time distributed systems. The contribution of RAS is twofold: First, it provides a formal system that integrates system's timing requirements and requirements propagation into the process of architectural modeling and design in such a way that allows us to systematically enforce that the requirements are met in every step of the design process. Second, it offers an incremental and more scaleable approach for design modeling. These two features together make RAS a suitable model for the design of C2 systems. We further present an incremental method for verifying timing properties of an RAS model that helps to reduce the complexity of analysis both at a given design level and across different design levels.
17964420
A learning process to the identification of feature points on Chinese characters
The paper describes a novel stroke extraction approach to identify the feature points of a character, using line-filtering and learning-based techniques. The line-filtering technique based on convolution operations with a set of one-dimensional (1D) Gabor templates efficiently extracts the stroke segments from noisy and degraded characters. Furthermore, the relationship between endpoints of stroke segments is modeled as junction structure during a learning process. Finally, each endpoint is identified as a feature point to determine the junction structure by the learning-based technique, rather than rule-based techniques with manual rule creation. Experimental results indicate that the learning-based technique can generalize learning knowledge to identify 1200 feature points with an average identification rate of 93.58% for test set, using k-fold cross-validation testing.
10320126
Resolving the unencoded character problem for chinese digital libraries
Constructing a Chinese digital library, especially for historical article archiving, is often hindered by the small character sets supported by current computer systems. This paper is aimed at resolving the unencoded character problem with a practical and composite approach for Chinese digital libraries. The proposed approach consists of the glyph expression model, glyph structure database, and supporting tools. With this approach, the following problems can be resolved. First, the extensibility of Chinese characters can be preserved. Second, it would be as easy to generate, input, display, and search unencoded characters as existing ones. Third, it is compatible with existing encoding schemes that most computers use. This approach has been utilized by organizations and projects in various application domains including archeology, linguistics, ancient texts, calligraphy and paintings, and stone and bronze rubbings. For example, in Academia Sinica, a very large full-text database of ancient texts called Scripta Sinica has been created using this approach. The Union Catalog of National Digital Archives Project (NDAP) dealt with the unencoded characters encountered when merging the metadata of 12 different thematic domains from various organizations. Also, in Bronze Inscriptions Research Team (BIRT) of Academia Sinica, 3,459 bronze inscriptions were added, which is very helpful to the education and research in historic linguistics
34616421
Performance analysis of traffic networks based on Stochastic Timed Petri Net models
A compositional method for modeling and evaluation of performance indices of traffic control systems based on Stochastic Timed Petri Nets (STPN) is presented. We use STPN to specify traffic and traffic control at an intersection and use a random distribution model to model the motion of vehicles in a road segment between any two consecutive intersections. The traffic control system is thus modeled as a composition of individual intersection models and segment distribution models. A technique is presented to incrementally evaluate the system's performance by analyzing intersections separately, according to a carefully selected order. The analysis technique conforms to the accepted practice of transportation research. Compared to existing Petri net models of traffic control systems, our technique dramatically reduces the complexity of analysis.