A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms

In this paper, a TSK-type recurrent fuzzy network (TRFN) structure is proposed. The proposal calls for a design of TRFN by either neural network or genetic algorithms depending on the learning environment. Set forth first is a recurrent fuzzy network which develops from a series of recurrent fuzzy if-then rules with TSK-type consequent parts. The recurrent property comes from feeding the internal variables, derived from fuzzy firing strengths, back to both the network input and output layers. In this configuration, each internal variable is responsible for memorizing the temporal history of its corresponding fuzzy rule. The internal variable is also combined with external input variables in each rule's consequence, which shows an increase in network learning ability. TRFN design under different learning environments is next advanced. For problems where supervised training data is directly available, TRFN with supervised learning (TRFN-S) is proposed, and neural network (NN) learning approach is adopted for TRFN-S design. An online learning algorithm with concurrent structure and parameter learning is proposed. With flexibility of partition in the precondition part, and outcome of TSK-type, TRFN-S has the admirable property of small network size and high learning accuracy. As to the problems where gradient information for NN learning is costly to obtain or unavailable, like reinforcement learning, TRFN with Genetic learning (TRFN-G) is put forward. The precondition parts of TRFN-G are also partitioned in a flexible way, and all free parameters are designed concurrently by genetic algorithm. Owing to the well-designed network structure of TRFN, TRFN-G, like TRFN-S, also is characterized by a high learning accuracy property. To demonstrate the superior properties of TRFN, TRFN-S is applied to dynamic system identification and TRFN-G to dynamic system control. By comparing the results to other types of recurrent networks and design configurations, the efficiency of TRFN is verified

A Hybri of genetic algorithm and particle swarm optimization for recurrent network design

An evolutionary recurrent network which automates the design of recurrent neural/fuzzy networks using a new evolutionary learning algorithm is proposed in this paper. This new evolutionary learning algorithm is based on a hybrid of genetic algorithm (GA) and particle swarm optimization (PSO), and is thus called HGAPSO. In HGAPSO, individuals in a new generation are created, not only by crossover and mutation operation as in GA, but also by PSO. The concept of elite strategy is adopted in HGAPSO, where the upper-half of the best-performing individuals in a population are regarded as elites. However, instead of being reproduced directly to the next generation, these elites are first enhanced. The group constituted by the elites is regarded as a swarm, and each elite corresponds to a particle within it. In this regard, the elites are enhanced by PSO, an operation which mimics the maturing phenomenon in nature. These enhanced elites constitute half of the population in the new generation, whereas the other half is generated by performing crossover and mutation operation on these enhanced elites. HGAPSO is applied to recurrent neural/fuzzy network design as follows. For recurrent neural network, a fully connected recurrent neural network is designed and applied to a temporal sequence production problem. For recurrent fuzzy network design, a Takagi-Sugeno-Kang-type recurrent fuzzy network is designed and applied to dynamic plant control. The performance of HGAPSO is compared to both GA and PSO in these recurrent networks design problems, demonstrating its superiority

Reduced Interval Type-2 Neural Fuzzy System UsingWeighted Bound-Set Boundary Operation forComputation Speedup and Chip Implementation

This paper proposes a reduced interval type-2neural fuzzy system using weighted bound-set boundaries(RIT2NFS-WB) for the simplification of type-reduction operations.The objective of this simplification is to reduce the systemtraining time in software implementation and chip size inhardware implementation, especially when the number of rulesis large. The antecedent part in the RIT2NFS-WB uses intervaltype-2 fuzzy sets (IT2FSs), and the consequent part can be of theTakagi–Sugeno–Kang (TSK) or Mamdani type.The RIT2NFS-WBis built through an online structure and parameter learningto improve model accuracy. In addition, the interpretability ofthe RIT2NFS-WB is improved by considering distributions ofthe IT2FSs in input variables. A distinguishability-oriented costfunction is used in parameter learning to generate distinguishableIT2FSs and improve semantics-based interpretability. Forhighly overlapped IT2FSs, they are merged to reduce the numberof IT2FSs and improve complexity-based interpretability.The software-implemented TSK-type RIT2NFS-WB is hardwareimplementedon a field-programmable gate array chip. To acceleratethe chip execution speed, the chip utilizes not only the parallelexecution properties of fuzzy rules and bound-set boundaries butthe pipeline technique as well. In particular, the flexibility of thechip is considered so that no redesign of the circuits is requiredwhen the RIT2NFS-WB is applied to different problems. The characteristicsof the software- and hardware-implemented RIT2NFSWBare verified through various examples and comparisons withvarious type-1 and interval type-2 fuzzy models

Multi-objective Continuous-Ant-Colony-Optimized FC forRobot Wall-Following Control

This paper proposes a multi-objective, rule-coded,advanced, continuous-ant-colony optimization (MO-RACACO)algorithm for fuzzy controller (FC) design and its application tomulti-objective, wall-following control for a mobile robot. In theMO-RACACO-based FC design approach, the number of rulesand all free parameters in each rule are optimized using the MORACACOalgorithm. This is a complex multi-objective optimizationproblem that considers both the optimization of discrete variables(number of rules) and continuous variables (rule parameters).To address this problem, the MO-RACACO uses a rule-codedindividual (solution) representation and a rule-based mutationoperation to find Pareto-optimal solutions with different numbersof rules. New solutions in the MO-RACACO are generatedusing a pheromone-level-based adaptive elite-tournament pathselection strategy followed by a Gaussian sampling operation. TheMO-RACACO-based FC design approach is applied to a multiobjective,wall-following problem for a mobile robot. Three objectivesare defined so that the robot is collision-free, maintains aconstant distance from the wall, and moves smoothly at a highspeed. This automatic design approach avoids the time-consumingmanual design of fuzzy rules and the exhaustive collection ofinput-output training pairs. The performance of the MORACACO-based control is verified through comparisons withvarious multi-objective population-based optimization algorithms(MOPOAs) in multi-objective FC optimization problems. Thisstudy also includes experiments that demonstrate robot wallfollowingcontrol using an actual mobile robot

Evolutionary Robot Wall-Following Control UsingType-2 Fuzzy Controller With Species-DE-ActivatedContinuous ACO

Abstract—This paper proposes evolutionary wall-following controlof a mobile robot using an interval type-2 fuzzy controller(IT2FC) with species-differential-evolution-activated continuousant colony optimization (SDE-CACO). Both the position and speedof a mobile robot are controlled by using two IT2FCs to improvenoise resistance ability. A new cost function is defined to accuratelyevaluate the wall-following performance of an evolutionaryIT2FC. Atwo-stage training approach is proposed that learns a positionIT2FC followed by a speed IT2FC to optimize both the wallfollowingaccuracy and the moving speed. The proposed learningapproach avoids the time consuming task of the exhaustive collectionof supervised input–output training pairs. All fuzzy rules aregenerated online using a clustering-based approach during the evolutionarylearning process. All of the free parameters in an onlinegeneratedIT2FC are optimized using SDE-CACO, in which anSDE mutation operation is incorporated within a continuous ACOto improve its explorative ability. The proposed SDE-CACO iscompared with various population-based optimization algorithmsto demonstrate its efficiency and effectiveness in the wall-followingcontrol problem. This study also includes experiments that demonstratewall-following control utilizing a real mobile robot

Evolutionary-Group-Based Particle-Swarm-Optimized Fuzzy Controller With Application to Mobile-Robot Navigation in Unknown Environments

This paper proposes an evolutionary-group-based particle-swarm-optimization (EGPSO) algorithm for fuzzy-controller (FC) design. The EGPSO uses a group-based framework to incorporate crossover and mutation operations into particle-swarm optimization. The EGPSO dynamically forms different groups to select parents in crossover operations, particle updates, and replacements. An adaptive velocity-mutated operation (AVMO) is incorporated to improve search ability. The EGPSO is applied to design all of the free parameters in a zero-order Takagi-Sugeno-Kang (TSK)-type FC. The objective of EGPSO is to improve fuzzy-control accuracy and design efficiency. Comparisons with different population-based optimizations of fuzzy-control problems demonstrate the superiority of EGPSO performance. In particular, the EGPSO-designed FC is applied to mobile-robot navigation in unknown environments. In this application, the robot learns to follow object boundaries through an EGPSO-designed FC. A simple learning environment is created to build this behavior without an exhaustive collection of input-output training pairs in advance. A behavior supervisor is proposed to combine the boundary-following behavior and the target-seeking behavior for navigation, and the problem of dead cycles is considered. Successful mobile-robot navigation in simulation and real environments verifies the EGPSO-designed FC-navigation approach