Sharif Digital Repository

In this paper design and evaluation of an adaptive critic- based neurofuzzy controller for the stabilizing periodic orbits of chaotic systems has been presented in detail. The main superiority of the proposed controller over previous analogous fuzzy logic controller design approaches, e.g., genetic fuzzy logic controller, is its online tuning characteristic and remarkable reduced amount of computations used for parameter adaptation, which makes it desirable for real time applications. Considering the simplicity of this controller and its independence from the system model, this control method has the advantage of online learning and control, and can be applied to a large variety of systems.... 

In this paper, first we present a new explanation for the relationship between logical circuits and artificial neural networks, logical circuits and fuzzy logic, and artificial neural networks and fuzzy inference systems. This shows us that neural networks are working in the same way as logical circuits when the connection between them is through the fuzzy logic. However, themain difference between them is that logical circuits can be constructed without using any kind of optimization-based learning methods. Based on these results, we propose a new neuro-fuzzy computing system. As verified by simulation results, it can effectively be implemented on the memristor crossbar structure and... 

Image logs provide useful information for fracture study in naturally fractured reservoir. Fracture dip, azimuth, aperture and fracture density can be obtained from image logs and have great importance in naturally fractured reservoir characterization. Imaging all fractured parts of hydrocarbon reservoirs and interpreting the results is expensive and time consuming. In this study, an improved method to make a quantitative correlation between fracture densities obtained from image logs and conventional well log data by integration of different artificial intelligence systems was proposed. The proposed method combines the results of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Neural... 

In this paper an approach based on system identification is used for fault detection in a nuclear reactor. A continuous-time Extended Kalman Filter (EKF) is presented, which allows the parameters of the nonlinear system to be estimated. Also a fault tolerant control system is designed for the nuclear reactor during power changes operation. The proposed controller is an adaptive critic-based neuro-fuzzy controller. Performance of the controller in terms of transient response and robustness against failures is very good and considerable  

In this paper, two different hybrid intelligent systems are applied to develop practical soft identifiers for modeling the tool-tissue force as well as the resulted maximum local stress in laparoscopic surgery. To conduct the system identification process, a 2D model of an in vivo porcine liver was built for different probing tasks. Based on the simulation, three different geometric features, i.e. maximum deformation angle, maximum deformation depth and width of displacement constraint of the reconstructed shape of the deformed body are extracted. Thereafter, two different fuzzy inference paradigms are proposed for the identification task. The first identifier is an adaptive co-evolutionary... 

Purpose: The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) to classify the operating faults of a hydraulic system. The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits. Design/methodology/approach: In the proposed methodology, first, the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms, i.e. a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy (CMA-ES) and... 

Human mind from system perspective deals with high dimensional complex world as an adaptive Multi-Input Multi-Output complex system. This view is theorized by reductionism theory in philosophy of mind, where the world is represented as logical combination of simpler sub-systems for human so that operate with less energy. On the other hand, Human usually uses linguistic rules to describe and manipulate his expert knowledge about the world; the way that is well modeled by Fuzzy Logic. But how such a symbolic form of knowledge can be encoded and stored in plausible neural circuitry? Based on mentioned postulates, we have proposed an adaptive Neuro-Fuzzy machine in order to model a rule-based... 

Fuzzy inference systems always suffer from the lack of efficient structures or platforms for their hardware implementation. In this paper, we tried to overcome this difficulty by proposing a new method for the implementation of the fuzzy rule-based inference systems. To achieve this goal, we have designed a multi-layer neuro-fuzzy computing system based on the memristor crossbar structure by introducing a new concept called the fuzzy minterm. Although many applications can be realized through the use of our proposed system, in this study we only show how the fuzzy XOR function can be constructed and how it can be used to extract edges from grayscale images. One main advantage of our... 

This study proposes a spiking neuro-fuzzy clustering system based on a novel spike encoding scheme and a compatible learning algorithm. In this system, we utilize an analog to binary encoding scheme that properly maps the concept of "distance" in multi-dimensional analog spaces to the concept of "dissimilarity " of binary bits in the equivalent binary spaces. When this scheme is combined with a novel binary to spike encoding scheme and a proper learning algorithm is applied, a powerful clustering algorithm is produced. This algorithm creates flexible fuzzy clusters in its analog input space and modifies their shapes to different convex shapes during the learning process. This system has... 

Active Learning Method (ALM) is a powerful fuzzy soft computing tool, developed originally in order to promote an engineering realization of human brain. This algorithm, as a macro-level brain imitation, has been inspired by some behavioral specifications of human brain and active learning ability. ALM is an adaptive recursive fuzzy learning algorithm, in which a complex Multi Input, Multi Output system can be represented as a fuzzy combination of several Single-Input, Single-Output systems. SISO systems as associative layer of algorithm capture partial spatial knowledge of sample data space, and enable a granular knowledge resolution tuning mechanism through the learning process. The... 

Content-based image retrieval (CBIR) makes use of low-level image features, such as color, texture and shape, to index images with minimal human interaction. Considering the gap between low-level image features and the high-level semantic concepts in the CBIR, we proposed an image retrieval system for brain magnetic resonance images based on saliency map. The saliency map of an image contains important image regions which are visually more conspicuous by virtue of their contrast with respect to surrounding regions. First, the proposed approach exploits the ant colony optimization (ACO) technique to measure the image's saliency through ants' movements on the image. The textural features are... 

Charpy impact energy of the produced Al6061-SiCp laminated nanocomposites by mechanical alloying was modeled by adaptive neuro-fuzzy interfacial systems (ANFIS) in both crack divider and crack arrester configurations. The model was constructed by training, validating and testing of 171 gathered input-target data. The thickness of layers, the number of layers, the adhesive type, the crack tip configuration and the content of SiC nanoparticles were five independent input parameters utilized for modeling. The output parameter was Charpy impact energy of the nanocomposites. The performance of the proposed models was evaluated by absolute fraction of variance, the absolute percentage error and... 

Content-based image retrieval (CBIR) makes use of image features, such as color, texture or shape, to index images with minimal human intervention. Content-based image retrieval can be used to locate medical images in large databases. In this paper, the fundamentals of the key components of content-based image retrieval systems are introduced first to give an overview of this area. Then, a case study which describes the methodology of a CBIR system for retrieving human brain magnetic resonance images, is presented. The proposed method is based on Adaptive Neuro-fuzzy Inference System (ANFIS) learning and could classify an image as normal and tumoral. This research uses the knowledge of CBIR... 

In this paper, a novel neuro-fuzzy computing system is proposed where its learning is based on the creation of fuzzy relations by using a new implication method without utilizing any exact mathematical techniques. Then, a simple memristor cross-bar-based analog circuit is designed to implement this neuro-fuzzy system which offers very interesting properties. In addition to high connectivity between neurons and being fault tolerant, all synaptic weights in our proposed method are always non-negative, and there is no need to adjust them precisely. Finally, this structure is hierarchically expandable, and it can do fuzzy operations in real time since it is implemented through analog circuits.... 

Nowadays, genetic disorders, like cancer and birth defects, are a great threat to human life. Since the first noticing of these types of diseases, many efforts have been made and researches performed in order to recognize them and find a cure for them. These disorders affect genes and they appear as abnormal traits in a genetic organism. In order to recognize abnormal genes, we need to predict splice sites in a DNA signal; then, we can process the genetic codes between two continuous splice sites and analyze the trait that it represents. In addition to abnormal genes and their consequent disorders, we can also identify other normal human traits like physical and mental features. So the... 

Islanding detection methods are divided into three main groups as remote, active and passive. Although passive schemes have larger Non Detection Zones (NDZ) relative to other schemes, they are more used in utilities due to their low costs and less PQ problems than other schemes. Passive Schemes are based on the measurements of passive system parameters. These parameters are measured at the point of common coupling (PCC). A new approach in passive techniques is the use of data-mining to classify the system parameters. In this paper, massive indices are collected by simulation of a practical distribution system in PSCAD/EMTP environment. These indices include voltage, frequency, current,...