Rock slope stability analyses using extreme learning neural network and terminal steepest descent algorithm

The analysis of rock slope stability is a classical problem for geotechnical engineers. However, for practicing engineers, proper software is not usually user friendly, and additional resources capable of providing information useful for decision-making are required. This study developed a convenient tool that can provide a prompt assessment of rock slope stability. A nonlinear input-output mapping of the rock slope system was constructed using a neural network trained by an extreme learning algorithm. The training data was obtained by using finite element upper and lower bound limit analysis methods. The newly developed techniques in this study can either estimate the factor of safety for a rock slope or obtain the implicit parameters through back analyses. Back analysis parameter identification was performed using a terminal steepest descent algorithm based on the finite-time stability theory. This algorithm not only guarantees finite-time error convergence but also achieves exact zero convergence, unlike the conventional steepest descent algorithm in which the training error never reaches zero.

Learning to minimize energy costs and maximize mechanical work in a bimanual coordination task

The authors addressed the hypothesis that economy in motor coordination is a learning phenomenon realized by both reduced energy cost for a given workload and more external work at the same prepractice metabolic and attentional energy expenditure. "Self-optimization" of movement parameters has been proposed to reflect learned motor adaptations that minimize energy costs. Twelve men aged 22.3 [+ or -] 3.9 years practiced a 90[degrees] relative phase, upper limb, independent ergometer cycling task at 60 rpm, followed by a transfer test of unpracticed (45 and 75 rpm) and self-paced cadences. Performance in all conditions was initially unstable, inaccurate, and relatively high in both metabolic and attentional energy costs. With practice, coordinative stability increased, more work was performed for the same metabolic and attentional costs, and the same work was done at a reduced energy cost. Self-paced cycling was initially below the metabolically optimal, but following practice at 60 rpm was closer to optimal cadence. Given the many behavioral options of the motor system in meeting a variety of everyday movement task goals, optimal metabolic and attentional energy criteria may provide a solution to the problem of selecting the most adaptive coordination and control parameters.

On the discrete time dynamics of a self-stabilizing MCA learning algorithm

The stability of minor component analysis (MCA) learning algorithms is an important problem in many signal processing applications. In this paper, we propose an effective MCA learning algorithm that can offer better stability. The dynamics of the proposed algorithm are analyzed via a corresponding deterministic discrete time (DDT) system. It is proven that if the learning rate satisfies some mild conditions, almost all trajectories of the DDT system starting from points in an invariant set are bounded, and will converge to the minor component of the autocorrelation matrix of the input data. Simulation results will be furnished to illustrate the theoretical results achieved.

A stable MCA learning algorithm

Minor component analysis (MCA) is an important statistical tool for signal processing and data analysis. Neural networks can be used to extract online minor component from input data. Compared with traditional algebraic  approaches, a neural network method has a lower computational complexity. Stability of neural networks learning algorithms is crucial to practical applications. In this paper, we propose a stable MCA neural networks learning algorithm, which has a more satisfactory numerical stability than some existing MCA algorithms. Dynamical behaviors of the proposed algorithm are analyzed via deterministic discrete time (DDT) method and the conditions are obtained to guarantee convergence. Simulations are carried out to illustrate the theoretical results achieved.

Using supervised and unsupervised techniques to determine groups of patients with different doctor-patient stability

Decision trees and self organising feature maps (SOFM) are frequently used to identify groups. This research aims to compare the similarities between any groupings found between supervised (Classification and Regression Trees - CART) and unsupervised classification (SOFM), and to identify insights into factors associated with doctor-patient stability. Although CART and SOFM uses different learning paradigms to produce groupings, both methods came up with many similar groupings. Both techniques showed that self perceived health and age are important indicators of stability. In addition, this study has indicated profiles of patients that are at risk which might be interesting to general practitioners.

A new sliding mode-based learning control scheme

 A new sliding mode-based learning control scheme for a class of SISO dynamic systems is developed in this paper. It is seen that, based on the most recent information on the closed-loop stability, a recursive learning chattering-free sliding mode controller can be designed to drive the closed-loop dynamics to reach the sliding mode surface in a finite time, on which the desired closed-loop dynamics with the zero-error convergence can be achieved.

Sliding mode-like learning control for SISO complex systems with T-S fuzzy models

In this paper, a sliding mode-like learning control scheme is developed for a class of single input single output (SISO) complex systems. First, the Takagi-Sugeno (T-S) fuzzy modelling technique is employed to model the uncertain complex dynamical systems. Second, a sliding mode-like learning control is designed to drive the sliding variable to converge to the sliding surface, and the system states can then asymptotically converge to zero on the sliding surface. The advantages of this scheme are that: 1) the information about the uncertain system dynamics and the system model structure is not required for the design of the learning controller; 2) the closed-loop system behaves with a strong robustness with respect to uncertainties; 3) the control input is chattering-free. The sufficient conditions for the sliding mode-like learning control to stabilise the global fuzzy model are discussed in detail. A simulation example for the control of an inverted pendulum cart is presented to demonstrate the effectiveness of the proposed control scheme.

A robust learning control for SISO Nonlinear systems with T-S Fuzzy Model : C02-robust control

In this paper, a robust learning control is developed for a class of single input single output (SISO) nonlinear systems with T-S fuzzy model. It is seen that the proposed sliding mode learning control with the powerful Lipshitz-like condition can guarantee the stability, convergence and robustness of the closed-loop system without involving any assumptions on uncertain system dynamics. In addition, theconcept that the local system with the maximum membership function dominates the system dynamic behaviours helps to greatly simplify the control system design. It will be further seen that the continuous learning control ensures the advantage of chattering-free that may occur in conventional sliding mode systems. Simulation examples are presented to demonstrate the effectiveness of the proposed learning control through the comparison with the H-infinity control.

Project and Module Based Teaching and Learning

This paper proposes a new teaching and learning approach-project and module based teaching and learning (PMBTL). The PMBTL approach incorporates the merits of project/problem based and module based learning methods, and overcomes the limitations of these methods. The correlation between teaching, learning, practice and assessment is emphasized in this approach, and new methods have been proposed accordingly. The distinct features of these new methods differentiate the PMBTL approach from conventional teaching approaches. Evaluation of this approach on practical teaching and learning activities demonstrates the effectiveness and stability of the approach in improving the performance and quality of teaching and learning. The approach proposed in this paper is also intuitive to the design of other teaching units.

Least squares learning and the US Treasury bill rate

Understanding how agents formulate their expectations about Fed behavior is important for market participants because they can potentially use this information to make more accurate estimates of stock and bond prices. Although it is commonly assumed that agents learn over time, there is scant empirical evidence in support of this assumption. Thus, in this paper we test if the forecast of the three month T-bill rate in the Survey of Professional Forecasters (SPF) is consistent with least squares learning when there are discrete shifts in monetary policy. We first derive the mean, variance and autocovariances of the forecast errors from a recursive least squares learning algorithm when there are breaks in the structure of the model. We then apply the Bai and Perron (1998) test for structural change to a forecasting model for the three month T-bill rate in order to identify changes in monetary policy. Having identified the policy regimes, we then estimate the implied biases in the interest rate forecasts within each regime. We find that when the forecast errors from the SPF are corrected for the biases due to shifts in policy, the forecasts are consistent with least squares learning. © 2014 Elsevier B.V.

Personality types and languages learning strategies: chameleons changing colours

This exploration of associations between the reported Language Learning Strategy (LLS) preferences of learners of English as a Second Language (ESL) and their personality types is positioned within the contention that the two are generally related. Our findings unequivocally support the existence of this relationship. Moreover, they also provide a platform from which to understand the contribution to learning a second language of two very commonly cited personality traits, introversion/extroversion and neuroticism. However, they also provide the basis for the important caution that the association between personality types and LLS is quite volatile. We have found that it is variation rather than unwavering stability that features in how personality traits apply as predictive of ESL learners' specific LLS preferences. Such prediction is specified even further by the particular contexts of ESL learning where the LLS are applied, for example for listening or speaking and whether this occurs inside or outside a classroom. The implications of these findings for ESL teaching and learning are discussed as is the explanatory power of the chameleon metaphor.

Learning stable weights for data of varying dimension

In this paper we develop a data-driven weight learning method for weighted quasi-arithmetic means where the observed data may vary in dimension.