Complex Fuzzy Rules Research Articles

THE NOVEL CFRG -BASED COMPLEX FUZZY TRANSFER LEARNING SYSTEM

Today, the rapid development of the internet has led to a data explosion; the complex fuzzy transfer learning (CFTL) model has received increasing attention from the academic community due to its various real-world applications, such as solar activity, digital signal processing, time series forecasting, etc. CFTL combines Transfer learning and Complex Fuzzy Logic in a framework to solve the problem of learning tasks with no prior direct contextual knowledge, which is stored, retrieved, and organized in the data structure. Data structures play an important role in computational intelligence because they are key performance indicators for systems or models. Therefore, to improve the performance of the previous CFTL, this paper investigates a novel complex fuzzy decision tree (CFDT) structure to represent the complex fuzzy rules and provides a transfer learning model for a complex fuzzy inference system. In contrast with prior axis-parallel decision trees in which only a single feature or variable is considered at each node, the node of the proposed decision tree structures includes complex fuzzy inference rules that contain multiple elements. Multiple features for each node help minimize the size. To prove the efficiency of the proposed framework, we carry out extension experiments on numerous instances (datasets). Experimental results demonstrate/exhibit that our offered performs better than the prior framework regarding accuracy and the size of the produced trees.

Speed Tracking Performance for a Coreless Linear Motor Servo System Based on a Fitted Adaptive Fuzzy Controller

Fuzzy control is widely used in linear motor servo systems. However, simple fuzzy rules reduce the control accuracy of the servo system, while complex fuzzy rules reduce the speed of its decision making. This paper proposes a fitted adaptive fuzzy controller (FAFC) to improve the speed tracking performance of a coreless linear motor servo system. The FAFC took a planned curve as a motion target. The planned curve is compounded by multiple performance curves of fuzzy control under the same given input. These multiple performance curves cover the variation range of motor parameters, which are offline-obtained. The performance of the planned curve is ensured by the multiple offline performance curves. The FAFC only needs simple fuzzy rules to fit the planned curve, and achieves high control accuracy without affecting the decision speed. The experimental results verified the feasibility of an FAFC. This research shows that an FAFC can effectively shorten the online calculation time of complex algorithms and keep the consistency of performance.

Automatic control method of automobile steering-by-wire based on fuzzy PID

Automatic driving is gradually being popularised, and automatic control of automobile steering by wire has also become a topic worthy of research. The research combines the fuzzy control algorithm and PID to construct a fuzzy PID control strategy, and integrates the genetic algorithm into the basic fuzzy rules to form an improved new algorithm. The algorithm mainly focuses on multiple changes in the steering-by-wire of the car, including angular momentum, angular velocity, linear velocity and linear acceleration, to build more complex fuzzy rules and then use the three most basic parameters of PID. The results show that the average accuracy rate, average recall rate and average precision rate of the improved algorithm are 91.27%, 73.67% and 80.94%, which are significantly higher than the other two algorithms for comparison, indicating that the improved algorithm has better performance.

FANCFIS: Fast adaptive neuro-complex fuzzy inference system

Large-scale multivariate time series forecasting is an increasingly important problem, with sensor networks pouring out sampled data at unprecedented rates, trillions of dollar's worth of stock trades made every data, and zettabytes of traffic transmitted over the Internet every year. While they are only examples of the much larger domain of general data streams, the uniformly-sampled time series still remains a very large and important subdomain. Extensive research has shown that machine-learning algorithms can often be very effective forecasting models, but many of these algorithms do not scale well. The Adaptive Neuro-Complex-Fuzzy Inferential System is one such approach; built to leverage complex fuzzy sets, it is both an accurate and parsimonious forecasting algorithm. However, its scaling is poor due to a relatively slow training algorithm (gradient descent hybridized with chaotic simulated annealing). Before the algorithm can be used for large-scale forecasting, a fast training algorithm that preserves the system's accuracy and compactness must be developed.We propose the Fast Adaptive Neuro-Complex Fuzzy Inference System, which is designed for fast training of a compact, accurate forecasting model. We use the Fast Fourier Transform algorithm to identify the dominant frequencies in a time series, and then create complex fuzzy sets to match them as the antecedents of complex fuzzy rules. Consequent linear functions are then learned via recursive least-squares. We evaluate this algorithm on both univariate and multivariate time series, finding that this incremental-learning algorithm is as accurate and compact as its slower predecessor, and can be trained much more quickly.

Competitive interaction reasoning: A bio-inspired reasoning method for fuzzy rule based classification systems

In designing fuzzy rule based classification systems (FRBCSs), complex fuzzy rule extraction techniques and tuning membership functions are frequently used to enhance classification accuracy. However, these approaches not only decrease system's transparency which is the hallmark of fizzy design, but also are oftentimes computationally expensive and require multiple parameters to be optimized using a large amount of training data. In this paper, inspired by the so-called competitive behavior of mini-columns in the brain neuronal circuitry, we proposed a new reasoning method for fuzzy classifiers referred to as Competitive Interaction Reasoning (CIR) that employs the cumulative information provided by all fuzzy rules and adjusts the decision boundaries as if the membership functions are directly modified. This mechanism is mathematically implemented by a linear transformation and resembles the competitive interaction observed in brain neuronal columns. Cross-rule competition weights are optimized using Hebbian reinforcement learning. Using a large number of simulations on benchmark data sets, we show that the proposed CIR significantly improves classification accuracy without compromising interpretability of the fuzzy classifier. In addition, CIR can further facilitate formation of the fuzzy rules and incorporation of the expert knowledge by confining the destructive effects of noisy rules or expert inconsistencies. Experiments on 23 well-known benchmark data sets confirm high performance of CIR in comparison with a number of popular classifiers.

ANCFIS: A Neurofuzzy Architecture Employing Complex Fuzzy Sets

Complex fuzzy sets (CFSs) are an extension of type-1 fuzzy sets in which the membership of an object to the set is a value from the unit disc of the complex plane. Although there has been considerable progress made in determining the properties of CFSs and complex fuzzy logic, there has yet to be any practical application of this concept. We present the adaptive neurocomplex-fuzzy-inferential system (ANCFIS), which is the first neurofuzzy system architecture to implement complex fuzzy rules (and, in particular, the signature property of rule interference). We have applied this neurofuzzy system to the domain of time-series forecasting, which is an important machine-learning problem. We find that ANCFIS performs well in one synthetic and five real-world forecasting problems and is also very parsimonious. Experimental comparisons show that ANCFIS is comparable with existing approaches on our five datasets. This work demonstrates the utility of complex fuzzy logic on real-world problems.

The concurrent implementation of high velocity control performance and high energy efficiency for hydraulic injection moulding machines

A high servo control response and a high energy efficiency, which are difficult to achieve simultaneously, are requested in hydraulic servo systems of current hydraulic injection moulding machines. This investigation develops new solutions to concurrently implement energy-saving control and velocity control for simultaneously realising high velocity control response and high energy efficiency for injection moulding machines. Two different hydraulic pump systems, including a variable displacement pump and a variable rotational speed pump, are developed and in each one of them two different energy-saving control concepts, such as load-sensing control and constant supply pressure control, are implemented. Thus, for verifying the feasibility of the integrated control systems and comparing the energy-saving effects, velocity control of the hydraulic cylinder can be integrated with four different energy-saving control systems (ESCSs) and also the conventional hydraulic system without energy-saving control. A fuzzy sliding mode control that can simplify the complex fuzzy rule bases is used for solving the complex two-input two-output systems. The experimental results show the excellent performance of the integrated concurrent control systems.