SIRMs (Single Input Rule Modules) Connected Fuzzy Inference Model

Stabilization control of series-type double inverted pendulum systems using the SIRMs dynamically connected fuzzy inference model

A new fuzzy controller for stabilization of parallel-type double inverted pendulum system

Yubazaki et al. have proposed "single input rule modules connected type fuzzy reasoning method" (SIRMs method, for short) whose final output is obtained by summarizing the product of the importance degrees and the inference results from single input fuzzy rule module. Another type of single input type fuzzy reasoning method proposed by Hayashi et al. (we call it "Single Input Connected fuzzy reasonig method" (SIC method, for short) in this paper) uses rule modules to each input item as well as SIRMs method. We expect that inference results of SIRMs method and SIC method have monotonicity if the antecedent parts and consequent parts of fuzzy rules in SIRMs rule modules have monotonicity. However, this paper points out that even if fuzzy rules in SIRMs rule modules have monotonicity, the inference results do not necessarily have monotonicity. Moreover, it clarifies the conditions for the monotonicity of inference results by SIRMs method and SIC method.

A fuzzy controller for stabilizing parallel-type double inverted pendulum system is presented, based on the single input rule modules (SIRMs) dynamically connected fuzzy inference model. By using the SIRMs and the dynamic importance degrees, the angular controls of the two pendulums and the position control of the cart are done entirely in parallel and the priority orders of the three controls are automatically adjusted according to control situations. Simulation results show that the fuzzy controller with a simple and intuitive structure can stabilize completely a parallel-type double inverted pendulum system within 10 seconds. This is the first result for a fuzzy controller to realize complete stabilization of a parallel-type double inverted pendulum system.

A stability analysis approach for a class of fuzzy controllers based on the single input rule modules (SIRMs) dynamically connected fuzzy inference model is proposed. It is proved that such a fuzzy controller can be approximately changed into a linear state feedback controller near an equilibrium point if each SIRM has a property of linear input-output mapping. Then linear state feedback control theories can be used to solve the stability problem. The proposed approach is applied to stabilization control of inverted pendulum system and the stability analysis results agree completely with the control results.

This paper presents the construction of an active suspension system for a quarter car model using fuzzy reasoning. The active control is obtained as a weighted sum of the defuzzificated values of the outputs in single input rule modules, and is generated by using a pneumatic actuator. The experimental result indicates that the proposed active control is more effective in the vibration isolation of the vehicle body than the skyhook damper and passive controls.

A broad-deep fusion network-based fuzzy emotional intention inference model for teaching validity evaluation

Environmental comprehensive assessment of agricultural systems at the farm level using fuzzy logic: A case study in cane farms in Iran

Adjusting the membership functions to satisfy one pattern may deteriorate the inference outcomes of the others. This incompatible issue can be retarded by the extension theory. A novel extension-based fuzzy modeling method, which differs from the traditional fuzzy inference, is proposed. Instead of directly applying the given data to building the fuzzy model, the given data are transformed to another domain by a sigmoidal function to obtain a better fuzzy model. We also define the extended correlation functions to relate the data with the fuzzy sets. During the refining process, the extended fuzzy model, which considers the positive and negative sets simultaneously, is adjusted by the gradient descent method. Simulation results from both single-input-single-output and double-input-single-output systems verified that better results than the conventional methods can be obtained.

The single-input-rule-modules (SIRMs) connected fuzzy reasoning method is an efficient way to reduce the number of fuzzy rules in the rule-based fuzzy controllers. However, there still exist fuzzy rule generation problems in the design process of the SIRMs connected fuzzy logic controllers (SIRMFLCs). To tackle this problem, this paper presents a prior information driven method for constructing the SIRMFLCs, including both type-2 and type-1 cases. Here, prior information means the requirements that the controllers must meet, e.g. continuity, symmetry and monotonicity of the controllers. To encode such prior information into the SIRMFLCs, the parameter conditions or constraints are provided. To show the effectiveness of the proposed method, applications to the stabilization control of the overhead crane system is given. Through utilizing the prior information to constrain the structures and parameters of fuzzy rules, the proposed strategy can reduce the blindness in the initialization of fuzzy rules and can generate the single input rule modules systematically.

In this paper, a new kind of fuzzy regression model suggested by the author in papers [1] and [2] will be restated more clearly. Then a consistency between the new fuzzy regression modeling and fuzzy inference modeling will be presented, showing its added advantage for application problems. Based on the consistency, a method for combining fuzzy regression and fuzzy inference will be introduced in this paper. This will serve as the starting point for applying the two kinds of information resources, quantitative and qualitative information resources, simultaneously in practical prediction problems.

A fuzzy decision tree method for fault classification in the steam generator of a pressurized water reactor

This paper introduces a novel fuzzy model - transductive neural-fuzzy classifier with weighted data normalization (TWNFC). While inductive approaches are concerned with the development of a model to approximate data in the whole problem space (induction), and consecutively - using this model to calculate the output value(s) for a new input vector (deduction), in transductive systems a local model is developed for every new input vector, based on some closest data to this vector from the training data set. The weighted data normalization method (WDN) optimizes the data normalization ranges for the input variables of a system. A steepest descent algorithm is used for training the TWNFC model. The TWNFC is illustrated on a case study: a real medical decision support problem of estimating the survival of haemodialysis patients. This personalized modeling can also be applied to other distance-based, prototype learning neural network or fuzzy inference models

To construction effective simulation meta-models for complex physical simulation system, the “curse of dimension” and the “uncertain and imprecise information” problems have to be addressed firstly. Although simulation meta-models based on neural networks can obtain well performance, the fuzzy inference mechanism of domain expert for practical application problems cannot be simulated. Thus, some prediction results may be contradicted with that of the actual physical systems. Aim at these problems, a novelty selective ensemble (SEN) simulation meta-modeling approach based on multiple kernel feature extraction and fuzzy inference modeling is proposed in this paper. A new ensemble construction method based on multiple candidate kernel latent features is used to produce candidate training sub-samples. Candidate sub-models based on Mamdani's fuzzy inference are constructed with these training sub-samples. Brand and bound (BB) and adaptive weighting fusion (AWF) algorithms are used to select and combine the ensemble sub-models to obtain final SEN simulation meta-model. Simulate results based on synthetic data show that the proposed approach is effective.

The geometric accuracy and surface roughness are mainly affected by the flank wear at the minor cutting edge in finish machining. A genetic algorithm-based fuzzy estimator obtained by a fuzzy inference algorithm to evaluate the minor flank wear length in finish milling is introduced. The fuzzy inference rules are trained by genetic algorithms (GA) through practice. Fuzzy membership functions and rules are usually decided upon subjectively. In this paper, the performance of the fuzzy estimator may be improved if the fuzzy inference model is supplemented by a genetic-based learning mechanism. The features sensitive to minor flank wear are extracted from the dispersion analysis of a time series AR model of the feed directional acceleration of the spindle housing. Linguistic rules for fuzzy estimation are constructed using these features, and then fuzzy inferences are carried out with test data sets under various cutting conditions. The proposed system turns out to be effective for estimating minor flank wear length, and its mean error is less than 13%.