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In order to avoid the over fitting and training and solve the knowledge extraction problem in fuzzy neural networks system. A Lazy Learning Dynamic Fuzzy Neural Network (LL-DFNN) algorithm is proposed. The Learning Set based on Lazy Learning is constituted from input and output. Then the framework of Lazy Leaning Dynamic Fuzzy Neural Network is designed and its stability is proved. Finally, Simulation...
This paper addresses the problem of real-time moving-object detection, classification and tracking in populated and dynamic environments. In this scenario, a mobile robot uses 2D laser range data to recognize, track and avoid moving targets. Most previous approaches either rely on pre-defined data features or off-line training of a classifier for specific data sets, thus eliminating the possibility...
Sliding mode control (SMC) of cleaning robot's mobile manipulator based on neural networks which have nonlinear approximation ability is put forward in this article. The controller reduces inherent chattering phenomenon sharply when the uncertainties and external disturbances are unknown. Structure of sliding mode control and neural networkspsila learning algorithms using Lyapunov theorem are designed...
A dynamic output feedback linearization technique for model reference control of nonlinear TITO (two-input two-output) systems identified by an Additive Nonlinear Autoregressive eXogenous (ANARX) model is proposed. ANARX structure of the model can be obtained by training a neural network of the specific restricted connectivity structure. Linear discrete-time reference model is given in the form of...
Aiming at the complex dynamic feature of large ship, an intelligent control structure based on library-similar knowledge-increasable neural network group is presented. This compounded control structure using the dynamic knowledge-increasable learning capability of the neural network groups, solve the problems of online identification and online design of the controller, so that the high precise output...
It was difficult to design a simple and effective learning algorithm based on gradient for PID neural networks because their neurons have discontinuous transfer functions. A new on-line algorithm was proposed according to the signal flow graph (SFG) theory in this paper. All gradients could be calculated directly from the SFGs of PID neural networks by this method. Moreover, an adaptive learning rate...
Several methods have been introduced for identification of nonlinear processes via locally or partially linear models. Unfortunately, most of these methods have a training phase which should be done offline. There are phenomena that possess time varying behavior. Furthermore, the amount, distribution and/or quality of measurement data that is available before the model is put to operation may be insufficient...
Dynamic multi-objective optimization (DMO) is one of the most challenging class of optimization problems where the objective functions change over time and the optimization algorithm is required to identify the corresponding Pareto optimal solutions with minimal time lag. DMO has received very little attention in the past and none of the existing multi-objective algorithms perform satisfactorily on...
In order to model the dynamics of a billet reheating furnace, a multi-input multi-output radial-basis-function neural network is constructed based on an improved sequential-learning algorithm. The algorithm employs an improved growing-and-pruning algorithm based on the concept of the significance of hidden neurons, and an extended Kalman filter improves the learning accuracy. Verification results...
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