Sharif Digital Repository

This paper is concerned with the design and simulation of an intelligent stop and go cruise control system in an automated vehicle. In this paper Active learning method is used to extract driver's behavior and to derive control rules for cruise control system. First, there is a brief introduction to ALM (Active Learning Method) and its specifications. Then a one-line space for driving is assumed and its parameters are extracted. By using IDS, the processing engine of ALM, effective parameters in controller are derived. A simulation program is written to produce learning samples and also to evaluate controller's parameters. To apply controller's output, appropriate acceleration of the... 

In this paper the Unsupervised Active Learning Method (UALM), a novel clustering method based on the Active Learning Method (ALM) is introduced. ALM is an adaptive recursive fuzzy learning algorithm inspired by some behavioral features of human brain functionality. UALM is a density-based clustering algorithm that relies on discovering densely connected components of data, where it can find clusters of arbitrary shapes. This approach is a noise-robust clustering method. The algorithm first blurs the data points as ink drop patterns, then summarizes the effects of all data points, and finally puts a threshold on the resulting pattern. It uses the connected-component algorithm for finding... 

This paper presents a new and simple algorithm for partitioning the input domain for implementation of Active Learning Method (ALM) algorithm. ALM is a pattern-based algorithm for soft computing which uses the Ink Drop Spread (IDS) algorithm as its main engine for feature extraction. In this paper a simple algorithm is introduced with a few computation cost. In order to evaluate the performance of the proposed algorithm, it is applied to two applications, system modeling and pattern recognition. Simulation results show the effectiveness of our algorithm in specifying the appropriate points for dividing the inputs domains  

This paper introduces an innovative adaptive controller based on the actor-critic method. The proposed approach employs the ink drop spread (IDS) method as its main engine. The IDS method is a new trend in softcomputing approaches that is a universal fuzzy modeling technique and has been also used as a supervised controller. Its process is very similar to the processing system of the human brain. The proposed actor-critic method uses an IDS structure as an actor and a 2-dimensional plane, representing control variable states, as a critic that estimates the lifetime goodness of each state. This method is fast, simple, and away from mathematical complexity. The proposed method uses the... 

In this study Active Learning Method (ALM) as a novel fuzzy modeling approach is compared with optimized Support Vector Machine (SVM) using simple Genetic Algorithm (GA), as a well known datadriven model for long term simulation of daily streamflow in Karoon River. The daily discharge data from 1991 to 1996 and from 1996 to 1999 were utilized for training and testing of the models, respectively. Values of the Nash-Sutcliffe, Bias, R 2, MPAE and PTVE of ALM model with 16 fuzzy rules were 0.81, 5.5 m 3 s -1, 0.81, 12.9%, and 1.9%, respectively. Following the same order of parameters, these criteria for optimized SVM model were 0.8, -10.7 m 3 s -1, 0.81, 7.3%, and -3.6%, respectively. The... 

In this paper a novel fuzzy approach exploiting Active Learning Method is employed in order to estimate the immeasurable states required to control a non-observable double inverted pendulum. Active Learning Method (ALM) is a fuzzy modeling method which exploits Ink Drop Spread (IDS) as its main engine. IDS is a universal fuzzy modeling technique which is very similar to the way human brain processes different phenomena. The ALM system is trained by the data obtained from Linear Quadratic Regulator (LQR) controller. LQR uses an optimal control approach which under certain conditions guarantees robustness. Instead of an expert's knowledge, the LQR controller output is used as a priori... 

This paper presents a high performance direct torque control (DTC) theme for the induction motor (IM). To solve those problems associated with conventional DTC, such as flux and torque ripple, variable switching frequency, inaccuracy in motor model and other parts of system. The Active Learning Method (ALM) is implemented on the DTC. In the Active Learning Method for information modeling, a method known as Ink Drop Spread (IDS) is used. The simulation results of DTC system based on ALM and the comparison of motor performance under the proposed control system with respect to those obtained under conventional DTC confirms its effectiveness and accuracy  

In this paper, we propose a novel density-based fuzzy clustering algorithm based on Active Learning Method (ALM), which is a methodology of soft computing inspired by some hypotheses claiming that human brain interprets information in pattern-like images rather than numerical quantities. The proposed clustering algorithm, Fuzzy Unsupervised Active Learning Method (FUALM), is performed in two main phases. First, each data point spreads in the feature space just like an ink drop that spreads on a sheet of paper. As a result of this process, densely connected ink patterns are formed that represent clusters. In the second phase, a fuzzifying process is applied in order to summarize the effects... 

In this paper, we propose a novel density-based fuzzy clustering algorithm based on Active Learning Method (ALM), which is a methodology of soft computing inspired by some hypotheses claiming that human brain interprets information in pattern-like images rather than numerical quantities. The proposed clustering algorithm, Fuzzy Unsupervised Active Learning Method (FUALM), is performed in two main phases. First, each data point spreads in the feature space just like an ink drop that spreads on a sheet of paper. As a result of this process, densely connected ink patterns are formed that represent clusters. In the second phase, a fuzzifying process is applied in order to summarize the effects... 

One of the main elements of a spoken dialogue system is the Spoken Language Understanding (SLU) unit. Hidden Vector State (HVS) is one of the popular statistical methods applied to the SLU component. Extended Hidden Vector State (EHVS) is an enhanced version of the HVS. Although both parsers need only abstract data annotation, it is quiet time consuming and difficult to label the data. Thus, we present a novel active learning method for the EHVS parser to reduce the human labeling effort. The active learner makes use of pattern classification to select the informative data based on four different uncertainty measures. Experiments are done on a Persian dataset, the University Information... 

In this investigation, the idea of Visual Working Memory (VWM) mechanism modeling based on versatile fuzzy method; Active Learning method, is presented. Visual information process; retrieving and learning rely on the use of Ink Drop Spread (IDS) and Center of Gravity (COG) as spatial density convergence operators. IDS modeling is characterized by processing that uses intuitive pattern information instead of complex formulas, and it is capable of stable and fast convergence. Furthermore, because it approves that distortion in retrieving irrelative data is adaptive to avoid storing lots of repetitive external information in daily visualization. Subsequently, this distortion is analyzed via two... 

This paper describes a genetic-fuzzy system adapted to find efficient partitions on data domains for IDS (Ink Drop Spread). IDS is the engine of Active Learning Method (ALM), a methodology of soft computing. IDS extracts useful information from a system subjected to modeling. Proposed method, called GIDS (Genetic IDS), uses genetic algorithm which optimizes the parameters of membership functions that represent the partitions on data planes. Obtained Results showed that using genetic algorithm to find the partitions has better accuracy than the previous generic IDS methods. © 2008 IEEE  

This paper presents a new inference algorithm for Active Learning Method (ALM). ALM is a pattern-based algorithm for soft computing which uses the Ink Drop Spread (IDS) algorithm as its main engine for feature extraction. In this paper a fuzzy number is extracted from each IDS plane rather than the narrow path and spread as in previous approaches. In order to compare performance of the proposed algorithm with the original one, two functions which are widely used in literature are modeled as the benchmark. Simulation results show that the proposed algorithm is as effective as previous one in the modeling task  

Fuzzy techniques can be used for accurate and high-speed modeling as well as for the control of complex systems, but various challenging problems are usually encountered during their actual implementation. For example, the variable parameters need to be optimized iteratively during the training phase, where this process is inspired by crisp domain algorithms. However, in recent years, memristor-based structures have emerged as another promising method for implementing neural network structures and fuzzy algorithms. In this study, we propose a novel adaptive and real-time fuzzy modeling algorithm, which employs the active learning method concept to mimic the functionality of the brain's right... 

Active Learning Method (ALM) is one of the powerful tools in soft computing and it is inspired by the human brain capabilities in approaching complicated problems. ALM, which is in essence an adaptive fuzzy learning algorithm, tries to model a Multi-Input Single-Output system with several single-input single-output subsystems. Each of these subsystems is then modeled by an ink drop spread (IDS) plane. IDS operator, which is the main processing engine of ALM, extracts two kinds of informative features, Narrow Path and Spread, from each IDS plane without complicated computations. These features from all IDS planes are then aggregated in the inference engine. Despite the great performance of... 

Active Learning Method (ALM) is one of the powerful tools in soft computing and it is inspired by the human brain capabilities in approaching complicated problems. ALM, which is in essence an adaptive fuzzy learning algorithm, tries to model a Multi-Input Single-Output system with several single-input single-output subsystems. Each of these subsystems is then modeled by an ink drop spread (IDS) plane. IDS operator, which is the main processing engine of ALM, extracts two kinds of informative features, Narrow Path and Spread, from each IDS plane without complicated computations. These features from all IDS planes are then aggregated in the inference engine. Despite the great performance of... 

Active learning method (ALM) is a powerful fuzzy-based soft computing methodology suitable for various applications such as function modeling, control systems, clustering and classification. Despite considerable advantages, the main computational engine of ALM, ink drop spread (IDS), is memory-intensive, which imposes significant area overheads in the hardware realization of the ALM for real-time applications. In this paper, we propose a compressed model for ALM which greatly alleviates the storage limitations. The proposed approach employs a distinct inference algorithm, enabling a significant reduction in memory utilization from O(N2) to O(2N) for a multi-input single-output (MISO) system.... 

In this paper, we propose a fuzzification method for clusters produced from a clustering process, based on Active Learning Method (ALM). ALM is a soft computing methodology which is based on a hypothesis claiming that human brain interprets information in pattern-like images. The proposed fuzzification method is applicable to all non-fuzzy clustering algorithms as a post process. The most outstanding advantage of this method is the ability to determine the membership degrees of each data to all clusters based on the density and shape of the clusters. It is worth mentioning that for existing fuzzy clustering algorithms such as FCM the membership degree is usually determined as a function of... 

In this paper, a new reinforcement learning approach is proposed which is based on a powerful concept named Active Learning Method (ALM) in modeling. ALM expresses any multi-input-single-output system as a fuzzy combination of some single-input-singleoutput systems. The proposed method is an actor-critic system similar to Generalized Approximate Reasoning based Intelligent Control (GARIC) structure to adapt the ALM by delayed reinforcement signals. Our system uses Temporal Difference (TD) learning to model the behavior of useful actions of a control system. The goodness of an action is modeled on Reward-Penalty-Plane. IDS planes will be updated according to this plane. It is shown that the...