Online Incremental Learning Research Articles

Integrating relevance vector machines and genetic algorithms for optimization of seed-separating process

A hybrid intelligent approach based on relevance vector machines (RVMs) and genetic algorithms (GAs) has been developed for optimal control of parameters of nonlinear manufacturing processes. It concerns the finding of the near-optimal control parameters of the nonlinear discrete manufacturing process with a specific objective. First, the nonlinear process with measurement noise is regressed by the relevance vector learning mechanism based on a kernel-based Bayesian framework. For minimizing the approximate error, uniform design sampling, online incremental learning and cross-validation are used in the learning process of RVMs. Such well-trained models become a specialized process simulation tool, which is valuable in prediction and optimization of nonlinear processes. Next, the near-optimal setpoints of the control system, which maximize the objective function, are sought by GAs from the numerous values of the objective function obtained from the simulation. As a case study, the seed separator system (5XZW-1.5) is used for evaluating the proposed intelligent approach. The control parameters to reach the maximum weighted objective, which combine the system output and evaluation functions, are optimized. The experimental results show the effectiveness of the proposed hybrid approach.

Learning fuzzy rules from iterative execution of games

This paper discusses the linguistic knowledge extraction from the iterative execution of a multi-player non-cooperative repeated game. Linguistic knowledge is automatically extracted in the form of fuzzy if–then rules. Our knowledge extraction is mainly based on the on-line incremental learning of fuzzy rule-based systems. In this sense, our linguistic knowledge extraction is the learning of fuzzy rules. We first briefly describe a market selection game, which is formulated as a non-cooperative repeated game with many players and several alternative actions. We also describe some simple strategies for our market selection game. In our market selection game, the payoff of each player depends on the actions of all players. When a particular action is chosen by many players, those players receive low payoff. High payoff is obtained from actions chosen by only a small number of players. This means that minority players with respect to their actions receive high payoff. Next we show how our market selection game can be handled as a pattern classification problem where a single training pattern is successively generated from every round of our game. A fuzzy rule-based classification system is used as a decision-making system by each player for choosing an action in every round. An on-line incremental learning algorithm is proposed for adjusting the fuzzy rule-based classification system. Then we show how our market selection game can be handled as a function approximation problem. A fuzzy rule-based approximation system is used as a value function for approximating the expected payoff from each action. Finally simulation results show that comprehensible linguistic knowledge is extracted by the learning of fuzzy rule-based systems.

HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems

This paper proposes an adaptive neuro-fuzzy system, HyFIS (Hybrid neural Fuzzy Inference System), for building and optimising fuzzy models. The proposed model introduces the learning power of neural networks to fuzzy logic systems and provides linguistic meaning to the connectionist architectures. Heuristic fuzzy logic rules and input–output fuzzy membership functions can be optimally tuned from training examples by a hybrid learning scheme comprised of two phases: rule generation phase from data; and rule tuning phase using error backpropagation learning scheme for a neural fuzzy system. To illustrate the performance and applicability of the proposed neuro-fuzzy hybrid model, extensive simulation studies of nonlinear complex dynamic systems are carried out. The proposed method can be applied to an on-line incremental adaptive learning for the prediction and control of nonlinear dynamical systems. Two benchmark case studies are used to demonstrate that the proposed HyFIS system is a superior neuro-fuzzy modelling technique.