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A comparison of fuzzy approaches to e-commerce review rating prediction
This paper presents a comparative analysis of the performance of fuzzy approaches on the task of predicting customer review ratings using a computational intelligence framework based on a genetic algorithm for data dimensionality reduction. The performance of the Fuzzy C-Means (FCM), a neurofuzzy approach combining FCM and the Adaptive Neuro Fuzzy Inference System (ANFIS), and the Simplified Fuzzy ARTMAP (SFAM) was compared on six datasets containing customer reviews. The results revealed that all computational intelligence predictors were suitable for the rating prediction problem, and that the genetic algorithm is effective in reducing the number of dimensions without affecting the prediction performance of each computational intelligence predictor
A Review of Fault Diagnosing Methods in Power Transmission Systems
Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field
Thermal error modelling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera
Thermal errors are often quoted as being the largest contributor to CNC machine tool errors, but they can be effectively reduced using error compensation. The performance of a thermal error compensation system depends on the accuracy and robustness of the thermal error model and the quality of the inputs to the model. The location of temperature measurement must provide a representative measurement of the change in temperature that will affect the machine structure. The number of sensors and their locations are not always intuitive and the time required to identify the optimal locations is often prohibitive, resulting in compromise and poor results.
In this paper, a new intelligent compensation system for reducing thermal errors of machine tools using data obtained from a thermal imaging camera is introduced. Different groups of key temperature points were identified from thermal images using a novel schema based on a Grey model GM (0, N) and Fuzzy c-means (FCM) clustering method. An Adaptive Neuro-Fuzzy Inference System with Fuzzy c-means clustering (FCM-ANFIS) was employed to design the thermal prediction model. In order to optimise the approach, a parametric study was carried out by changing the number of inputs and number of membership functions to the FCM-ANFIS model, and comparing the relative robustness of the designs. According to the results, the FCM-ANFIS model with four inputs and six membership functions achieves the best performance in terms of the accuracy of its predictive ability. The residual value of the model is smaller than Âą 2 Îźm, which represents a 95% reduction in the thermally-induced error on the machine. Finally, the proposed method is shown to compare favourably against an Artificial Neural Network (ANN) model
Mathematical tools for identifying the fetal response to physical exercise during pregnancy
In the applied mathematics literature there exist a significant number of tools that can reveal the interaction between mother and fetus during rest and also during and after exercise. These tools are based on techniques from a number of areas such as signal processing, time series analysis, neural networks, heart rate variability as well as dynamical systems and chaos. We will briefly review here some of these methods, concentrating on a method of extracting the fetal heart rate from the mixed maternal-fetal heart rate signal, that is based on phase space reconstructio
Wireless Interference Identification with Convolutional Neural Networks
The steadily growing use of license-free frequency bands requires reliable
coexistence management for deterministic medium utilization. For interference
mitigation, proper wireless interference identification (WII) is essential. In
this work we propose the first WII approach based upon deep convolutional
neural networks (CNNs). The CNN naively learns its features through
self-optimization during an extensive data-driven GPU-based training process.
We propose a CNN example which is based upon sensing snapshots with a limited
duration of 12.8 {\mu}s and an acquisition bandwidth of 10 MHz. The CNN differs
between 15 classes. They represent packet transmissions of IEEE 802.11 b/g,
IEEE 802.15.4 and IEEE 802.15.1 with overlapping frequency channels within the
2.4 GHz ISM band. We show that the CNN outperforms state-of-the-art WII
approaches and has a classification accuracy greater than 95% for
signal-to-noise ratio of at least -5 dB
PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles
There exists an increasing demand for a flexible and computationally
efficient controller for micro aerial vehicles (MAVs) due to a high degree of
environmental perturbations. In this work, an evolving neuro-fuzzy controller,
namely Parsimonious Controller (PAC) is proposed. It features fewer network
parameters than conventional approaches due to the absence of rule premise
parameters. PAC is built upon a recently developed evolving neuro-fuzzy system
known as parsimonious learning machine (PALM) and adopts new rule growing and
pruning modules derived from the approximation of bias and variance. These rule
adaptation methods have no reliance on user-defined thresholds, thereby
increasing the PAC's autonomy for real-time deployment. PAC adapts the
consequent parameters with the sliding mode control (SMC) theory in the
single-pass fashion. The boundedness and convergence of the closed-loop control
system's tracking error and the controller's consequent parameters are
confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's
efficacy is evaluated by observing various trajectory tracking performance from
a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing
micro aerial vehicle called hexacopter. Furthermore, it is compared to three
distinctive controllers. Our PAC outperforms the linear PID controller and
feed-forward neural network (FFNN) based nonlinear adaptive controller.
Compared to its predecessor, G-controller, the tracking accuracy is comparable,
but the PAC incurs significantly fewer parameters to attain similar or better
performance than the G-controller.Comment: This paper has been accepted for publication in Information Science
Journal 201
Adaptive Neuro-Fuzzy Inference System for Dynamic Load Balancing in 3GPP LTE
ANFIS is applicable in modeling of key parameters when investigating the performance and functionality of wireless networks. The need to save both capital and operational expenditure in the management of wireless networks cannot be over-emphasized. Automation of network operations is a veritable means of achieving the necessary reduction in CAPEX and OPEX. To this end, next generations networks such WiMAX and 3GPP LTE and LTE-Advanced provide support for self-optimization, self-configuration and self-healing to minimize human-to-system interaction and hence reap the attendant benefits of automation. One of the most important optimization tasks is load balancing as it affects network operation right from planning through the lifespan of the network. Several methods for load balancing have been proposed. While some of them have a very buoyant theoretical basis, they are not practically implementable at the current state of technology. Furthermore, most of the techniques proposed employ iterative algorithm, which in itself is not computationally efficient. This paper proposes the use of soft computing, precisely adaptive neuro-fuzzy inference system for dynamic QoS-aware load balancing in 3GPP LTE. Three key performance indicators (i.e. number of satisfied user, virtual load and fairness distribution index) are used to adjust hysteresis task of load balancing
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