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In recent years, several frameworks and systems have been proposed that extend Inductive Logic Programming (ILP) to the Answer Set Programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis together with a given background knowledge. In existing systems, the background knowledge is the same for all examples; however, examples may be context-dependent. This means that some examples should be explained in the context of some information, whereas others should be explained in different contexts. In this paper, we capture this notion and present a context-dependent extension of the Learning from Ordered Answer Sets framework. In this extension, contexts can be used to further structure the background knowledge. We then propose a new iterative algorithm, ILASP2i, which exploits this feature to scale up the existing ILASP2 system to learning tasks with large numbers of examples. We demonstrate the gain in scalability by applying both algorithms to various learning tasks. Our results show that, compared to ILASP2, the newly proposed ILASP2i system can be two orders of magnitude faster and use two orders of magnitude less memory, whilst preserving the same average accuracy. [ABSTRACT FROM AUTHOR]

Remotely sensed hyperspectral image classification is a very challenging task due to the spatial correlation of the spectral signature and the high cost of true sample labeling. In light of this, the collective inference paradigm allows us to manage the spatial correlation between spectral responses of neighboring pixels, as interacting pixels are labeled simultaneously. The transductive inference paradigm allows us to reduce the inference error for the given set of unlabeled data, as sparsely labeled pixels are learned by accounting for both labeled and unlabeled information. In this paper, both these paradigms contribute to the definition of a spectral-relational classification methodology for imagery data. We propose a novel algorithm to assign a class to each pixel of a sparsely labeled hyperspectral image. It integrates the spectral information and the spatial correlation through an ensemble system. For every pixel of a hyperspectral image, spatial neighborhoods are constructed and used to build application-specific relational features. Classification is performed with an ensemble comprising a classifier learned by considering the available spectral information (associated with the pixel) and the classifiers learned by considering the extracted spatio-relational information (associated with the spatial neighborhoods). The more reliable labels predicted by the ensemble are fed back to the labeled part of the image. Experimental results highlight the importance of the spectral-relational strategy for the accurate transductive classification of hyperspectral images and they validate the proposed algorithm. [ABSTRACT FROM AUTHOR]

Sensor networks, communication and financial networks, web and social networks are becoming increasingly important in our day-to-day life. They contain entities which may interact with one another. These interactions are often characterized by a form of autocorrelation, where the value of an attribute at a given entity depends on the values at the entities it is interacting with. In this situation, the collective inference paradigm offers a unique opportunity to improve the performance of predictive models on network data, as interacting instances are labeled simultaneously by dealing with autocorrelation. Several recent works have shown that collective inference is a powerful paradigm, but it is mainly developed with a fully-labeled training network. In contrast, while it may be cheap to acquire the network topology, it may be costly to acquire node labels for training. In this paper, we examine how to explicitly consider autocorrelation when performing regression inference within network data. In particular, we study the transduction of collective regression when a sparsely labeled network is a common situation. We present an algorithm, called CORENA (COllective REgression in Network dAta), to assign a numeric label to each instance in the network. In particular, we iteratively augment the representation of each instance with instances sharing correlated representations across the network. In this way, the proposed learning model is able to capture autocorrelations of labels over a group of related instances and feed-back the more reliable labels predicted by the transduction in the labeled network. Empirical studies demonstrate that the proposed approach can boost regression performances in several spatial and social tasks. [ABSTRACT FROM AUTHOR]

In some large enterprises introducing radical innovation may prove difficult, but introducing a combination of incremental changes may be more practical, particularly in the services sector where existing resources are utilized, and this may be seen as a process of entrepreneurial bricolage. For small resource-limited firms there may be no alternative but to draw on novel combinations of existing resources. The term bricolage comes from a French expression for 'tinkering' and this is what it is suggested many innovative SMEs do - learn-by-doing. The notion of entrepreneurial bricolage has been used to describe a process for assembling readily available physical and knowledge assets in novel combinations for a business purpose, creating product and process 'recipes'. In this paper, we explore the research question: How can entrepreneurial bricolage be represented as a coherent process? [ABSTRACT FROM AUTHOR]

In this paper, as a fundamental problem in smart grid, the residential load scheduling is studied in a comprehensive way. The main contributions lie in threefold. First, three indices, i.e., the power consumption expense, the robustness of schedule subject to uncertain electricity price and the satisfaction of customer, are taken into full consideration. We propose to optimize simultaneously the three indices via convex optimization. Second, iterative learning is utilized to setting parameters in the objective function, and keeps a proper tradeoff between the consumption expense and the satisfaction index. Third, in order to fully characterize the operation states of appliances, both binary and continuous variables are used, which results in a hybrid mixed-integer quadratic optimization problem. The relaxation technique is utilized to tackle the hybrid optimization problem. Theoretical analysis of the performance gap based on the proposed approach is provided as well. The performance of the proposed approach is illustrated by simulations. The parameter settings reflect actual preference and consumption manner of the consumer. In addition, both peak-to-average ratio of power load and variation of power load are reduced. [ABSTRACT FROM AUTHOR]

The present work investigates the relationship of iterative learning with other learning criteria such as decisiveness, caution, reliability, non-U-shapedness, monotonicity, strong monotonicity and conservativeness. Building on the result of Case and Moelius that iterative learners can be made non-U-shaped, we show that they also can be made cautious and decisive. Furthermore, we obtain various special results with respect to one-one texts, fat texts and one-one hypothesis spaces. [ABSTRACT FROM AUTHOR]

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Extreme learning machine (ELM) is a single-hidden layer feed-forward neural network with an efficient learning algorithm. Conventionally an ELM is trained using all the data based on the least square solution, and thus it may suffer from overfitting. In this paper, we present a new method of data and feature mixed ensemble based extreme learning machine (DFEN-ELM). DFEN-ELM combines data ensemble and feature subspace ensemble to tackle the overfitting problem and it takes advantage of the fast speed of ELM when building ensembles of classifiers. Both one-class and two-class ensemble based ELM have been studied. Experiments were conducted on computed tomography (CT) data for liver tumor detection and segmentation as well as magnetic resonance imaging (MRI) data for rodent brain segmentation. To improve the ensembles with new training data, sequential kernel learning is adopted further in the experiments on CT data for speedy retraining and iteratively enhancing the image segmentation performance. Experiment results on different testing cases and various testing datasets demonstrate that DFEN-ELM is a robust and efficient algorithm for medical object detection and segmentation. [ABSTRACT FROM AUTHOR]

Increasing demand for micro/nano-technology related equipment resulted in growing interest for precision positioning systems. In this paper a modular controller combining cross-coupled control and iterative learning control approaches to improve contour and tracking accuracy at the same time is presented. Instead of using the standard error estimation technique, a computationally efficient and modular contour error estimation technique is used. The new controller is more suitable for tracking arbitrary nonlinear contours and easier to implement to multi-axis systems. Stability and convergence analysis for the proposed controller is presented with the necessary conditions. Effectiveness of the control design is verified with simulations and experiments on a two-axis positioning system. The resulting positioning system achieves nanometer level contouring and tracking performance. [ABSTRACT FROM AUTHOR]

In this study, the estimation problem of periodically occurring faults is investigated for a class of non‐linear time‐varying systems. Unlike most existing results, the system subject to bounded disturbance and measurement noise is considered. A novel non‐linear iterative learning (IL) observer is constructed by using previous output estimation errors and inputs for the purpose of fault estimation (FE). The estimated fault is updated through a novel designed IL law. The recursive analysis and robust control technology are used to prove the condition under which the ultimate boundedness of the FE error can be guaranteed. Finally, a simulation study on a practical manipulator is provided to demonstrate the effectiveness and applicability of the proposed FE scheme. [ABSTRACT FROM AUTHOR]

Arbitrary high precision is considered one of the most desirable control objectives in the relative formation for many networked industrial applications, such as flying spacecrafts and mobile robots. The main purpose of this paper is to present design guidelines of applying the iterative schemes to develop distributed formation algorithms in order to achieve this control objective. If certain conditions are met, then the control input signals can be learned by the developed algorithms to accomplish the desired formations with arbitrary high precision. The systems under consideration are a class of multi-agent systems under directed networks with switching topologies. The agents have discrete-time affine nonlinear dynamics, but their state functions do not need to be identical. It is shown that the learning processes resulting from the relative output formation of multi-agent systems can converge exponentially fast with the increase of the iteration number. In particular, this work induces a distributed algorithm that can simultaneously achieve the desired relative output formation between agents and regulate the movement of multi-agent formations as desired along the time axis. The illustrative numerical simulations are finally performed to demonstrate the effectiveness and performance of the proposed distributed formation algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces a new control algorithm for the trajectory control of an electrohydraulic actuator (EHA). The key feature of this paper is the combination of a modified backstepping control with an iterative learning mechanism to perform adaptive tracking control tasks for a symmetrical pump-controlled EHA. Firstly, a mathematical model of the EHA is developed and a strictly feedback form state space is obtained. Next, the control signal is formed based on an iterative learning scheme with a backstepping modifier. Then, stability analyses are also carried out to ensure the convergence of the closed loop system. Finally, four experimental cases of studies are done to evaluate the proposed control method. [ABSTRACT FROM AUTHOR]

The parameters of traffic flow model are important to the management and control of urban road network. In order to more accurately describe the actual operation of traffic flow in urban road network, this paper proposes a nonlinear macroscopic traffic flow model containing unknown time-varying multi-parameter on the basis of the steady-state and dynamic characteristics of traffic flow, and designs a time-varying multi-parameter iterative learning identification strategy by using the inherent repetitive characteristics of traffic flow. In the finite time interval, the iterative learning identification strategy is used to transform the parameter identification problem into the optimal tracking control problem, so that the number of queuing vehicles at each entrance of intersection tends to the true value, thus improving the accuracy of the model. Finally, the convergence of the algorithm is proved by a strict theoretical derivation, and the effectiveness of the method is further verified by simulation experiments using the model-based control method. [ABSTRACT FROM AUTHOR]

The aircraft model shows obvious time-varying characteristic due to the uncertainty of aerodynamic parameters. The fault diagnosis of the flight control systems is a difficult issue. A sensor fault estimation approach based on aerodynamic parameter identification and iterative learning is proposed by taking the longitudinal motion model of some unmanned aerial vehicle as the study subject. The augmented cubature Kaiman filter ( ACKF) is used for the aerodynamic parameter estimation so that the system model can be identified online. Once a fault comes up, the currently identified aerodynamic parameters are applied to system modeling in the local flight envelope, and a fault estimator is constructed using the iterative learning algorithm. Furthermore, a novel iterative learning algorithm based on the essence of extended state observer (ESO) is designed to improve the fault estimation speed. The fault simulation experiments are conducted to verify the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper analyzes the relationships between typical undergraduate business statistics subjects and how they are used in downstream courses and beyond, using commonly taught business and economics courses as stations for reinforcing statistical concepts through applications. The role of accreditation in steering statistical applications is considered, with three levels of accreditors allowing programs to self-select into teaching, application, and research. The traditional approach of training undergraduate business majors for careers in industry, with possible graduate training later in the career, is the example used in this paper. [ABSTRACT FROM AUTHOR]

This paper considers the cooperative tracking of linear multi-agent systems with a dynamic leader whose input information is unavailable to any followers. Cooperative iterative learning controllers, based on the relative state information of neighboring agents, are proposed for tracking the dynamic leader over directed communication topologies. Stability and convergence of the proposed controllers are established using Lyapunov-Krasovskii functionals. Furthermore, this result is extended to the output feedback case where only the output information of each agent can be obtained. A local observer is constructed to estimate the unmeasurable states. Then, cooperative iterative learning controllers, based on the relative observed states of neighboring agents, are devised. For both cases, it is shown that the multi-agent systems whose communication topologies contain a spanning tree can reach synchronization with the dynamic leader, and meanwhile identify the unknown input of the dynamic leader using distributed iterative learning laws. An illustrative example is provided to verify the proposed control schemes. [ABSTRACT FROM AUTHOR]

Research on Nicaraguan Sign Language, created by deaf children, has suggested that young children use gestures to segment the semantic elements of events and linearize them in ways similar to those used in signed and spoken languages. However, it is unclear whether this is due to children’s learning processes or to a more general effect of iterative learning. We investigated whether typically developing children, without iterative learning, segment and linearize information. Gestures produced in the absence of speech to express a motion event were examined in 4-year-olds, 12-year-olds, and adults (all native English speakers). We compared the proportions of gestural expressions that segmented semantic elements into linear sequences and that encoded them simultaneously. Compared with adolescents and adults, children reshaped the holistic stimuli by segmenting and recombining their semantic features into linearized sequences. A control task on recognition memory ruled out the possibility that this was due to different event perception or memory. Young children spontaneously bring fundamental properties of language into their communication system. [ABSTRACT FROM PUBLISHER]

For the single phase inductance-capacitance-inductance (LCL) grid-connected inverter in micro-grid, a kind of robust iterative learning controller is designed. Based on the output power droop characteristics of inverter, the current sharing among the inverters is achieved. Iterative learning strategy is suitable for repeated tracking control and inhibiting periodic disturbance, and is designed using robust performance index, so that it has the ability to overcome the uncertainty of system parameters. Compared with the repetitive control, the robust iterative learning control can get high precision output waveform, and enhance the tracking ability for waveform, and the distortion problem of the output signal can be solved effectively. [ABSTRACT FROM AUTHOR]

Abstract: Automatic classes are classes of languages for which a finite automaton can decide whether a given element is in a set given by its index. The present work studies the learnability of automatic families by automatic learners which, in each round, output a hypothesis and update a long-term memory, depending on the input datum, via an automatic function. Many variants of automatic learners are investigated: where the long-term memory is restricted to be the current hypothesis whenever this exists, cannot be of length larger than the length of the longest datum seen, or has to consist of a constant number of examples seen so far. Learnability is also studied with respect to queries which reveal information about past data or past computation history; the number of queries per round is bounded by a constant. [Copyright &y& Elsevier]

Proposed in this paper is a new economic production lot size (EPLS) model, i.e., a multi-cycle flexible production/inventory model under random demands, which considers the holding and shortage costs of finished products as well as the setup costs of production, and employs an (s,Q) policy for production/inventory control. We have theoretically proved that the total expected cost of the model is a convex function with respect to variables s and Q. On this basis, an iterative learning algorithm is designed to speed up the process by using the model convexity, which enables us to obtain the optimal production preparation point and the optimal production quantity. Results from the comparison between the proposed algorithm and the genetic algorithm (GA) show that the control quantities secured by the two algorithms are consistent but the former has higher solution speed, which verifies the accuracy and effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

This study investigates the speed trajectory tracking problem of high‐speed trains with actuator failures and unknown speed delays as well as control input saturations. New adaptive iterative learning fault‐tolerant control (AILFTC) strategy is derived without the need for precise system parameters or analytically estimating bound on actuator failures variables. It is shown that with the proposed method, both actuator failures can be accommodated and the unknown time‐varying speed delays and control input saturations can be analysed by means of Lyapunov–Krasovskii function. As such, the resultant control algorithms are able to achieve the L[0,T]2 convergence of the train speed to desired profile during operations repeatedly in the presence of non‐linearities and parametric uncertainties, as validated by the theoretical analysis and numerical simulations. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to broaden the application’s area of artificial intelligence including fuzzy logic and multiobjective evolutionary algorithm into real-time control area. Wiper system is a high order, nonlinear model with single-input and multi-outputs so that rise time, maximum overshoot, and end-point vibration of wiper blade are observed in conflict as the faster response leads to the larger level of undesired noise and vibration. The first part of this paper centers acquiring experimental data from a passenger automobile wiper system during its operation and using a reliable nonlinear system identification, namely, nonlinear autoregressive exogenous Elman neural network. Knowing that in a practical environment, where the loading conditions of the flexible wiper blade may be varied due to rain, snow, or wind lift in high-speed driving, causing changes in the characteristics of the system, the system performance with a fixed conventional controller scheme will not be satisfactory. The main contribution of this work is presented in second part where a novel multiobjective, bilevel adaptive-fuzzy controller is proposed for an automobile wiper system. The system’s parameters are tuned simultaneously by a multiobjective genetic algorithm based on fitness sharing whereby an automobile wiper blade is moved within its sweep workspace in the least amount of time with minimum noise and vibration. [ABSTRACT FROM AUTHOR]

• Short-term load forecasting (STLF) of power loads involves fractal features, due to complexity of power systems. • Composite linear fractal interpolation function (CLFIF) modeling better captures irregularities and fluctuations of power loads. • Iterative learning (IL) and chimp optimization algorithm (ChOA) are used for adaptive parametrization of CLFIF. • Hybrid CLFIF-IL-ChOA modeling is proposed for STLF, which obtains high accuracy without further relevant data. • Comparisons in between CLFIF-IL-ChOA and others are included; effectiveness of CLFIF-IL-ChOA is verified. This paper develops a hybrid short-term load forecasting (STLF) modeling with adaptive parametrization, based on composite linear fractal interpolation function (CLFIF), iterative learning (IL) and chimp optimization algorithm (ChOA). More precisely, after selecting similar days in power load data, an amendatory CLFIF model is constructed for an-hour-ahead prediction in terms of hourly load curves. Then, iterative learning based on ChOA optimizes the parameters of the amendatory CLFIF model with higher accuracy. Moreover, to confirm effectiveness of the CLFIF-IL-ChOA model, numerical examples are tested on the historical power load data from PJM and ENTSOE. The numerical results show that the proposed method can obtain higher accuracy, compared to some common methods about time series analysis. [ABSTRACT FROM AUTHOR]

The uncertainties, force ripple, friction and other external disturbances in permanent magnet linear synchronous motors (PMLSMs) pose great challenges to the design of advanced controllers with high speed and high precision. Existing neural network based sliding mode control (NNSMC) methods typically adopt neural network as an online compensator for uncertainties and disturbances, meanwhile stability and robustness are assured owing to sliding mode control. However, PMLSMs usually need to perform repetitive motion tasks within a finite time interval in practical applications, where existing NNSMC methods ignore the previous repetitions' information, and consequently limit the motion performance improvement for trajectory tracking tasks. This paper proposes an iterative learning based neural network sliding mode control (ILNNSMC) scheme, in which the concept of iterative learning is incorporated into NNSMC methods for the first time. In ILNNSMC, the uncertainties and disturbances can be compensated by NN (updating in the time-domain) and ILC (learning in the iteration-domain) in a complementary way, and the sliding variable is driven to converge to zero for arbitrary time within the predesigned time interval with successive iterations. The motion performance for repetitive tasks can be improved substantively. Moreover, a non-linear dynamic learning gain (DLG) strategy is proposed and adopted in iterative learning law, which guarantees both convergence speed and steady-state accuracy in the learning process. Stability of the scheme is discussed in both time and iteration domains, and the control performance is verified by extensive experiments on a PMLSM. • Iterative learning is incorporated into neural network sliding mode control. • Cross-domain compensation is realized by neural network and iterative learning. • Dynamic learning gain trades off convergence speed and steady-state accuracy. • Stability is proved based on Lyapunov direct method in time and iteration domains. • Experimental results on a PMLSM verify the validity for repetitive tasks. [ABSTRACT FROM AUTHOR]

Abstract: This paper studies Fisher linear discriminants (FLDs) based on classification accuracies for imbalanced datasets. An optimal threshold is found out from a series of empirical formulas developed, which is related not only to sample sizes but also to distribution regions. A mixed binary–decimal coding system is suggested to make the very dense datasets sparse and enlarge the class margins on condition that the neighborhood relationships of samples are nearly preserved. The within-class scatter matrices being or approximately singular should be moderately reduced in dimensionality but not added with tiny perturbations. The weight vectors can be further updated by a kind of epoch-limited (three at most) iterative learning strategy provided that the current training error rates come down accordingly. Putting the above ideas together, this paper proposes a type of integrated FLDs. The extensive experimental results over real-world datasets have demonstrated that the integrated FLDs have obvious advantages over the conventional FLDs in the aspects of learning and generalization performances for the imbalanced datasets. [Copyright &y& Elsevier]

Abstract: We analyze iterative learning in the limit from positive data with the additional information provided by a counter. The simplest type of counter provides the current iteration number (counting up from 0 to infinity), which is known to improve learning power over plain iterative learning. We introduce five other (weaker) counter types, for example only providing some unbounded and non-decreasing sequence of numbers. Analyzing these types allows one to understand which properties of a counter learning can benefit from. For the iterative setting, we completely characterize the relative power of the learning criteria corresponding to the counter types. In particular, for our types, the only properties improving learning power are unboundedness and strict monotonicity. Furthermore, we show that each of our types of counter improves learning power over weaker ones in some settings; to this end, we analyze transductive and non-U-shaped learning. Finally we show that, for iterative learning criteria with one of our types of counter, separations of learning criteria are necessarily witnessed by classes containing only infinite languages. [Copyright &y& Elsevier]

George Box was fascinated with how we make discoveries. His path-breaking contributions to experimental design made statistics an active partner in the process of discovery. Box introduced us to response surface methods, evolutionary operation, resolution and rotatability, projective properties and design robustness. He developed popular experimental plans like the central composite and Box-Behnken designs. He explored the consequences of imperfect models and derived D-optimal designs for experiments to estimate mechanistic models. Box's ideas grew from close collaborations with scientists and engineers and have been applied successfully in a wide range of disciplines. He has left an indelible stamp on the field of experimental design and on the practice of scientific investigation. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

To alter the traditional large caliber naval gun flanking path shell-feeding mode which was complex, an abdominal shell-feeding method using center single line shell-swinging mechanism was proposed. According to the dynamic characteristics analysis of the abdominal shell-swinging mechanism, the secondary coupling term of the longitudinal deformation in the description of deformation field was considered to establish the first order approximation rigid- flexible coupling dynamics model. Then the iterative learning input shaping control method was proposed based on the first-order approximation theory, which joint PD feedback control to study the residual vibrations suppression of the abdominal shell-swinging arm. Numerical simulation of the residual vibrations control on different fire angles show that recursive learning input shaping method has a significant inhibitory effect to the vibrations on the case of large deformation. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose an iterative self-organizing map (SOM) approach with robust distance estimation (ISOMRD) for spatial outlier detection. Generally speaking, spatial outliers are irregular data instances which have significantly distinct non-spatial attribute values compared to their spatial neighbors. In our proposed approach, we adopt SOM to preserve the intrinsic topological and metric relationships of the data distribution to seek reasonable spatial clusters for outlier detection. The proposed iterative learning process with robust distance estimation can address the high dimensional problems of spatial attributes and accurately detect spatial outliers with irregular features. To verify the efficiency and robustness of our proposed algorithm, comparative study of ISOMRD and several existing approaches are presented in detail. Specifically, we test the performance of our method based on four real-world spatial datasets. Various simulation results demonstrate the effectiveness of the proposed approach. [Copyright &y& Elsevier]

In this study, computing system performance enhancement by using iterative learning technique is presented. Computational response time and throughput of the computing system is improved by introducing computational cost model and selection probability for each individual job. Excepted gain by enforcing dynamic caching is maximized in terms of classifying the arriving computing jobs on an selective manner and dynamically replacing them in a limited memory space. Gain maximization is performed by tuning the window size which helps to compare the computing jobs in terms of their individual selection and occurence probabilities. Fairly special computing work in insurance risk investigation is chosen for experimental validation of the proposed approach. Aspect Oriented Programming (AOP) methodology on Java platform is used for the experimental setup. AOP allows to identify computational jobs and their parameters based on the marked annotations. Experimental results show that the developed iterative learning based caching algorithm performs better than the other well known caching techniques. [ABSTRACT FROM AUTHOR]

Abstract: Iterative learning is a model of language learning from positive data, due to Wiehagen. When compared to a learner in Gold’s original model of language learning from positive data, an iterative learner can be thought of as memory-limited. However, an iterative learner can memorize some input elements by coding them into the syntax of its hypotheses. A main concern of this paper is: to what extent are such coding tricks necessary? One means of preventing some such coding tricks is to require that the hypothesis space used be free of redundancy, i.e., that it be 1–1. In this context, we make the following contributions. By extending a result of Lange and Zeugmann, we show that many interesting and non-trivial classes of languages can be iteratively identified using a Friedberg numbering as the hypothesis space. (Recall that a Friedberg numbering is a 1–1 effective numbering of all computably enumerable sets.) An example of such a class is the class of pattern languages over an arbitrary alphabet. On the other hand, we show that there exists an iteratively identifiable class of languages that cannot be iteratively identified using any 1–1 effective numbering as the hypothesis space. We also consider an iterative-like learning model in which the computational component of the learner is modeled as an enumeration operator, as opposed to a partial computable function. In this new model, there are no hypotheses, and, thus, no syntax in which the learner can encode what elements it has or has not yet seen. We show that there exists a class of languages that can be identified under this new model, but that cannot be iteratively identified. On the other hand, we show that there exists a class of languages that cannot be identified under this new model, but that can be iteratively identified using a Friedberg numbering as the hypothesis space. [Copyright &y& Elsevier]

Abstract: This paper presents an approach using a multi-objective controller to prevent noise and vibration generated by the wiper blade during its wiping operation. Firstly, this paper focuses on the experimental approach to collect noise and vibration data from a car wiper system during its operation and secondly, to develop black box model of the wiper system using nonparametric approach of system identification known as nonlinear auto regressive exogenous Elman neural network (NARXENN). Finally, a novel closed loop iterative input shaping controller whose parameters are tuned simultaneously by a Pareto based multi objective genetic algorithm (MOGA) are proposed and simulated in such a way that it can prevent unwanted noise and vibration in the wiper system. The main contribution of this work rather the previous studies of automobile wiper system is to develop a novel multi-objective control strategy whereby an automobile wiper blade is moved within its sweep workspace in the least amount of time with minimum noise and vibration. [Copyright &y& Elsevier]

Abstract: The belief rule-base inference methodology using evidential reasoning (RIMER) approach has been proved to be an effective extension of traditional rule-based expert systems and a powerful tool for representing more complicated causal relationships using different types of information with uncertainties. With a predetermined structure of the initial belief rule-base (BRB), the RIMER approach requires the assignment of some system parameters including rule weights, attribute weights, and belief degrees using experts’ knowledge. Although some updating algorithms were proposed to solve this problem, it is still difficult to find an optimal compact BRB. In this paper, a novel updating algorithm is proposed based on iterative learning strategy for delayed coking unit (DCU), which contains both continuous and discrete characteristics. Daily DCU operations under different conditions are modeled by a BRB, which is then updated using iterative learning methodology, based on a novel statistical utility for every belief rule. Compared with the other learning algorithms, our methodology can lead to a more optimal compact final BRB. With the help of this expert system, a feedforward compensation strategy is introduced to eliminate the disturbance caused by the drum-switching operations. The advantages of this approach are demonstrated on the UniSim™ Operations Suite platform through the developed DCU operation expert system modeled and optimized from a real oil refinery. [Copyright &y& Elsevier]

A novel approach to nanoscale broadband viscoelastic spectroscopy is presented. The proposed approach utilizes the recently developed modeling-free inversion-based iterative control (MIIC) technique to achieve accurate measurement of the material response to the applied excitation force over a broad frequency band. Scanning probe microscope (SPM) and nanoindenter have become enabling tools to quantitatively measure the mechanical properties of a wide variety of materials at nanoscale. Current nanomechanical measurement, however, is limited by the slow measurement speed: the nanomechanical measurement is slow and narrow-banded and thus not capable of measuring rate-dependent phenomena of materials. As a result, large measurement (temporal) errors are generated when material is undergoing dynamic evolution during the measurement. The low-speed operation of SPM is due to the inability of current approaches to (1) rapidly excite the broadband nanomechanical behavior of materials, and (2) compensate for the convolution of the hardware adverse effects with the material response during high-speed measurements. These adverse effects include the hysteresis of the piezo actuator (used to position the probe relative to the sample); the vibrational dynamics of the piezo actuator and the cantilever along with the related mechanical mounting; and the dynamics uncertainties caused by the probe variation and the operation condition. In the proposed approach, an input force signal with frequency characteristics of band-limited white-noise is utilized to rapidly excite the nanomechanical response of materials over a broad frequency range. The MIIC technique is used to compensate for the hardware adverse effects, thereby allowing the precise application of such an excitation force and measurement of the material response (to the applied force). The proposed approach is illustrated by implementing it to measure the frequency-dependent plane-strain modulus of poly(dimethylsiloxane) (PDMS) over a broad frequency range extending over 3 orders of magnitude (~1 Hz to 4.5 kHz). [ABSTRACT FROM AUTHOR]