Active learning of soft rules for system modelling

Using rule learning algorithms to model systems has gained considerable interest in the past. The underlying idea of active learning is to learning algorithm influence the selection of training examples. The presented method estimates the utility of new experiments based on the knowledge represented by the existing rulebase. An extended rule format allows to deal with uncertainty. Experiments with different artificial system functions show that the presented method improves the model quality respectively decreases the number of experiments needed to reach a specific level of performance

Time series forecasting using a TSK fuzzy system tuned with simulated annealing

In this paper, a combination of a Takagi-Sugeno fuzzy system (TSK) and simulated annealing is used to predict well known time series by searching for the best configuration of the fuzzy system. Simulated annealing is used to optimise the parameters of the antecedent and the consequent parts of the fuzzy system rules. The results of the proposed method are encouraging indicating that simulated annealing and fuzzy logic are able to combine well in time series prediction

Highway Entrance Ramp Monitoring and Control using Soft Computing Techniques

Highway entrance ramp monitoring and control is used to regularize the flow of traffic entering through the highway entrance ramps. It detects the flow of traffic enter ing through the highway ramp monitoring system and controls the flow of traffic using ramp monitoring and control algorithm. This algorithm employs some soft computing techniques which is a suitable choice due to the imprecise information and the level of inaccuracy in the monitoring data available. The use of AI - based search algorithms to calibrate the parameters of a micro - simulation model or estimate the dynamic demand needed to run the model is an area that has received significant attention from researchers recently. Use of soft computing techniques improves the traffic flow as also increases the efficiency of the traffic system with enhanced control a nd safety

Soft Computing Tool Approach for Texture Classification Using Discrete Cosine Transform

Texture can be considered as a repeating pattern of local variation of pixel intensities. Cosine Transform (DCT) coefficients of texture images. As DCT works on gray level images, the color scheme of each image is transformed into gray levels. For classifying the images using DCT, two popular soft computing techniques namely neurocomputing and neuro-fuzzy computing are used. A feedforward neural network is used to train the backpropagation learning algorithm and an evolving fuzzy neural network to classify the textures. The soft computing models were trained using 80% of the texture data and the remaining was used for testing and validation purposes. A performance comparison was made among the soft computing models for the texture classification problem. In texture classification the goal is to assign an unknown sample image to a set of known texture classes. It is observed that the proposed neuro-fuzzy model performed better than the neural network

Application of Neuro-Fuzzy Controller to Autonomous Agricultural Vehicle Operating on Unstructured Changing Terrain -Control Software Development-

The control of an autonomous agricultural vehicle operating on unstructured changing terrain includes many objective diffi culties. One major diffi culty concerns the characteristics of the terrain condition that the vehicle should operate in. Problems ranged from the effects of varying terrain conditions on the autonomous vehicle sensors and traction performance through to the need to deal with the presence of unexpected situations. On unstructured changing terrain, many factors infl uence vehicle behavior such as terrain slope, lateral slippage, and so on. Therefore, it is necessary to develop a more suitable model for vehicle motion on these terrain conditions. In order to control the vehicle along a course on unstructured changing terrain, it was developed control software to enable more accurate control. The developed method to control the vehicle when operating on these conditions was Neuro-Fuzzy Controller. Result of the trained model could be described as follows; number of nodes was 193, number of fuzzy rules was 81, average testing error between simulation and ANFIS output was 0.76, while for experimental and ANFIS output was 1.61. It was concluded that the developed control system had a good accuracy to steer the vehicle

Nonlinear Adaptive Signal Processing Improves the Diagnostic Quality of Transabdominal Fetal Electrocardiography

The abdominal fetal electrocardiogram (fECG) conveys valuable information that can aid clinicians with the diagnosis and monitoring of a potentially at risk fetus during pregnancy and in childbirth. This chapter primarily focuses on noninvasive (external and indirect) transabdominal fECG monitoring. Even though it is the preferred monitoring method, unlike its classical invasive (internal and direct) counterpart (transvaginal monitoring), it may be contaminated by a variety of undesirable signals that deteriorate its quality and reduce its value in reliable detection of hypoxic conditions in the fetus. A stronger maternal electrocardiogram (the mECG signal) along with technical and biological artifacts constitutes the main interfering signal components that diminish the diagnostic quality of the transabdominal fECG recordings. Currently, transabdominal fECG monitoring relies solely on the determination of the fetus’ pulse or heart rate (FHR) by detecting RR intervals and does not take into account the morphology and duration of the fECG waves (P, QRS, T), intervals, and segments, which collectively convey very useful diagnostic information in adult cardiology. The main reason for the exclusion of these valuable pieces of information in the determination of the fetus’ status from clinical practice is the fact that there are no sufficiently reliable and well-proven techniques for accurate extraction of fECG signals and robust derivation of these informative features. To address this shortcoming in fetal cardiology, we focus on adaptive signal processing methods and pay particular attention to nonlinear approaches that carry great promise in improving the quality of transabdominal fECG monitoring and consequently impacting fetal cardiology in clinical practice. Our investigation and experimental results by using clinical-quality synthetic data generated by our novel fECG signal generator suggest that adaptive neuro-fuzzy inference systems could produce a significant advancement in fetal monitoring during pregnancy and childbirth. The possibility of using a single device to leverage two advanced methods of fetal monitoring, namely noninvasive cardiotocography (CTG) and ST segment analysis (STAN) simultaneously, to detect fetal hypoxic conditions is very promising

Analysis of Hybrid Soft Computing Techniques for Intrusion Detection on Network

Intrusion detection is an action towards security of a network when a system or network is being used inappropriately or without authorization. The use of Soft Computing Approaches in intrusion detection is an Appealing co ncept for two reasons: firstly, the Soft Computing Approaches achieve tractability, robustness, low solution cost, and better report with reality. Secondly, current techniques used in network security from intrusion are not able to cope with the dynamic and increasingly complex nature of network and their security. It is hoped that Soft Computing inspired approaches in this area will be able to meet this challenge. Here we analyze the approaches including the examination of efforts in hybrid system of SC su ch as neuro - fuzzy, fuzzy - genetic, neuro - genetic, and neuro - fuzzy - genetic used the development of the systems and outcome their implementation. It provides an introduction and review of the key developments within this field, in addition to making suggestio ns for future research