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.
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.
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.
6599744
A multilayer recurrent neural network for solving continuous-time algebraic Riccati equations
A multilayer recurrent neural network is proposed for solving continuous-time algebraic matrix Riccati equations in real time. The proposed recurrent neural network consists of four bidirectionally connected layers. Each layer consists of an array of neurons. The proposed recurrent neural network is shown to be capable of solving algebraic Riccati equations and synthesizing linear-quadratic control systems in real time. Analytical results on stability of the recurrent neural network and solvability of algebraic Riccati equations by use of the recurrent neural network are discussed. The operating characteristics of the recurrent neural network are also demonstrated through three illustrative examples.
7304743
Proxy ecology - cooperative proxies with artificial life
In this paper, we propose a novel P2P cooperative proxy cache system using an individual-based model. We borrow the idea from an ecological system as well as economic systems to manage the cooperative proxies through data and information exchange among individual proxies. The data flow among proxy nodes creates artificial life for the cooperative proxies. The proxy servers with artificial life can automatically configure themselves into a virtual proxy graph. The aggregate effect of caching actions by individual peer proxies automatically distributes the Web document closer to the clients and balances the workload. Our simulation results show that the proposed proxy caching scheme tremendously improves the system performance. In addition, the individual-based design model ensures the simplicity and scalability of the cache system.
16538539
Optimal jumper insertion for antenna avoidance under ratio upper-bound
Antenna effect may damage gate oxides during plasma-based fabrication process. The antenna ratio of total exposed antenna area to total gate oxide area is directly related to the amount of damage. Jumper insertion is a common technique applied at routing and post-layout stages to avoid and to fix the problems caused by the antenna effect. This paper presents an optimal algorithm for jumper insertion under the ratio upper-bound. It handles Steiner trees with obstacles. The algorithm us based on dynamic programming while works on free trees. The time complexity is O(alpha|V|2 ) and the space complexity is O(|V|2), where |V| is the number of nodes in the routing tree and a is a factor depending on how to find a non-blocked position on a wire for a jumper
22535159
Interactive Cross-Language Document Selection
The problem of finding documents written in a language that the searcher cannot read is perhaps the most challenging application of cross-language information retrieval technology. In interactive applications, that task involves at least two steps: (1) the machine locates promising documents in a collection that is larger than the searcher could scan, and (2) the searcher recognizes documents relevant to their intended use from among those nominated by the machine. This article presents the results of experiments designed to explore three techniques for supporting interactive relevance assessment: (1) full machine translation, (2) rapid term-by-term translation, and (3) focused phrase translation. Machine translation was found to better support this task than term-by-term translation, and focused phrase translation further improved recall without an adverse effect on precision. The article concludes with an assessment of the strengths and weaknesses of the evaluation framework used in this study and some remarks on implications of these results for future evaluation campaigns.
18182406
Recurrent neural networks for minimum infinity-norm kinematic control of redundant manipulators
This paper presents two neural network approaches to minimum infinity-norm solution of the velocity inverse kinematics problem for redundant robots. Three recurrent neural networks are applied for determining a joint velocity vector with its maximum absolute value component being minimal among all possible joint velocity vectors corresponding to the desired end-effector velocity. In each proposed neural network approach, two cooperating recurrent neural networks are used. The first approach employs two Tank-Hopfield networks for linear programming. The second approach employs two two-layer recurrent neural networks for quadratic programming and linear programming, respectively. Both the minimal 2-norm and infinity-norm of joint velocity vector can be obtained from the output of the recurrent neural networks. Simulation results demonstrate that the proposed approaches are effective with the second approach being better in terms of accuracy and optimality.