Weights Allocation Based on Optimal Uncertainty Evaluation

In the traditional measurement theory, precision is defined as the dispersion of measured value, and is used as the basis of weights calculation in the adjustment of measurement data with different qualities, which leads to the trouble that trueness is completely ignored in the weight allocation. In this paper, following the pure concepts of probability theory, the measured value (observed value) is regarded as a constant, the error as a random variable, and the variance is the dispersion of all possible values of an unknown error. Thus, a rigorous formula for weights calculation and variance propagation is derived, which solves the theoretical trouble of determining the weight values in the adjustment of multi-channel observation data with different qualities. The results show that the optimal weights are not only determined by the covariance array of observation errors, but also related to the model of adjustment.

Particle Swarm Optimization of BP-ANN Based Soft Sensor for Greenhouse Climate

In this article, the authors develop the Particle Swarm Optimization algorithm (PSO) in order to optimise the BP network in order to elaborate an accurate dynamic model that can describe the behavior of the temperature and the relative humidity under an experimental greenhouse system. The PSO algorithm is applied to the Back-Propagation Neural Network (BP-NN) in the training phase to search optimal weights baded on neural networks. This approach consists of minimising the reel function which is the mean squared difference between the real measured values of the outputs of the model and the values estimated by the elaborated neural network model. In order to select the model which possess higher generalization ability, various models of different complexity are examined by the test-error procedure. The best performance is produced by the usage of one hidden layer with fourteen nodes. A comparison of measured and simulated data regarding the generalization ability of the trained BP-NN model for both temperature and relative humidity under greenhouse have been performed and showed that the elaborated model was able to identify the inside greenhouse temperature and humidity with a good accurately.

Financial stress effects on financial markets: dynamic connectedness and portfolio hedging

PurposeThis paper investigates the impact of financial stress on the dynamic connectedness and hedging for oil market and stock-bond markets of the Gulf Cooperation Council (GCC).Design/methodology/approachThis study uses the wavelet coherence model to examine the interactions between financial stress, oil and GCC stock and bond markets. Second, the authors apply the time–frequency connectedness developed by Barunik and Krehlik (2018) so as to identify the direction and scale connectedness among these markets. Third, the authors examine the optimal weights, hedge ratio and hedging effectiveness for oil and financial markets based on constant conditional correlation (CCC), dynamic conditional correlation (DCC) and Baba-Engle-Kraft-Kroner (BEKK)-GARCH models.FindingsThe authors have found that the correlation between the oil and stock-bond markets tends to be stable in nonshock periods, but it evolves during oil and financial shocks at lower frequencies. Moreover, the authors find that the oil market and financial stress are the main transmitters of risks. The connectedness is mainly driven by the long term, demonstrating that the markets rapidly process the financial stress spillover effect, and the shock is transmitted over the long run. Optimal weights show different patterns for each negative and positive case of the financial stress index. In the negative (positive) financial stress case, investors should have more oil (stocks) than stocks (oil) in their portfolio in order to minimize risk.Originality/valueThis study has gone some way toward enhancing one’s understanding of the time–frequency connectedness between the financial stress, oil and GCC stock-bond markets. Second, it identifies the impact of financial stress into hedging strategies offering important insights for investors aiming at managing and reducing portfolio risk.

An improved weighted optimization approach for large-scale global optimization

It is a great challenge for ordinary evolutionary algorithms (EAs) to tackle large-scale global optimization (LSGO) problems which involve over hundreds or thousands of decision variables. In this paper, we propose an improved weighted optimization approach (LSWOA) for helping solve LSGO problems. Thanks to the dimensionality reduction of weighted optimization, LSWOA can optimize transformed problems quickly and share the optimal weights with the population, thereby accelerating the overall convergence. First, we concentrate on the theoretical investigation of weighted optimization. A series of theoretical analyses are provided to illustrate the search behavior of weighted optimization, and the equivalent form of the transformed problem is presented to show the relationship between the original problem and the transformed one. Then the factors that affect problem transformation and how they take affect are figured out. Finally, based on our theoretical investigation, we modify the way of utilizing weighted optimization in LSGO. A weight-sharing strategy and a candidate solution inheriting strategy are designed, along with a better allocation of computational resources. These modifications help take full advantage of weighted optimization and save computational resources. The extensive experimental results on CEC’2010 and CEC’2013 verify the effectiveness and scalability of the proposed LSWOA.

Computational Analysis of Genetic Code Variations Optimized for the Robustness against Point Mutations with Wobble-like Effects

It is believed that the codon–amino acid assignments of the standard genetic code (SGC) help to minimize the negative effects caused by point mutations. All possible point mutations of the genetic code can be represented as a weighted graph with weights that correspond to the probabilities of these mutations. The robustness of a code against point mutations can be described then by means of the so-called conductance measure. This paper quantifies the wobble effect, which was investigated previously by applying the weighted graph approach, and seeks optimal weights using an evolutionary optimization algorithm to maximize the code’s robustness. One result of our study is that the robustness of the genetic code is least influenced by mutations in the third position—like with the wobble effect. Moreover, the results clearly demonstrate that point mutations in the first, and even more importantly, in the second base of a codon have a very large influence on the robustness of the genetic code. These results were compared to single nucleotide variants (SNV) in coding sequences which support our findings. Additionally, it was analyzed which structure of a genetic code evolves from random code tables when the robustness is maximized. Our calculations show that the resulting code tables are very close to the standard genetic code. In conclusion, the results illustrate that the robustness against point mutations seems to be an important factor in the evolution of the standard genetic code.

Comprehensive Review & Enhancements on Probe Substitution Techniques for Faulty Steady State Inlet Pressure Distortion Data

When a gas-turbine engine is in operation, inlet-generated total-pressure distortion can have a detrimental effect on engine’s stability and performance. During the product development life cycle, on-ground wind tunnel tests and in-flight tests are performed to estimate the inlet distortion characteristics. Extensive measures are taken in the preparation and execution of inlet distortion tests. The data pertaining to spatial inlet distortion is recorded using an array of high-response total-pressure probes. The pressure probes are usually arranged in rake and ring arrays as per AIR1419. The data from these probes is used by propulsion system designers to address the effects of inlet distortion on stability and performance, particularly the engine’s sensitivity to inlet distortion. In some instances, the probes can produce inaccurate measurements or no measurements at all, due to a variety of reasons. This may result in a time consuming and costly process of repeating the test. To avoid this, the inaccurate or invalid measurements can be substituted using a variety of statistical techniques during test data post-processing. This paper discusses the results of different interpolation techniques to substitute invalid steady-state total-pressure measurements, evaluated in the context of classical distortion profile data available in AIR1419. The techniques include 1D linear interpolation using only probes data from adjacent rings, 1D linear interpolation using only probes data from adjacent rakes, and bilinear interpolation using probes data from adjacent rings and rakes. Furthermore, the paper evaluates a bilinear interpolation technique with optimal weights obtained from linear regression, that enhances the estimation of invalid pressure values.

Intelligent Prediction Model: Optimized Neural Network for Lean Manufacturing Technology

Lean manufacturing (LM) is a method, which focuses on reducing wastes and increasing the productivity within manufacturing firms. Several analyses were performed on LM technology depending on minimal lead times, enhanced quality and reduced operating costs. However, limitation exists in understanding its role to develop managing commitment, worker involvement and in turn its organizational performance. This paper intends to propose a new Neural Network (NN) based intelligent prediction framework. The initial process is manual labeling or response validation, which is carried out by utilizing the responses attained for each questions under each factors including lean awareness, employee involvement, management commitment, lean technology, Organizational Performance (OP) and Organizational Support (OS). Subsequently, NN is exploited for prediction process, where the features (received responses) are given as input and the labeling values attained are set as target. Further, in order to improve the prediction performance, the NN training is performed by a new Hybrid Particle Swarm and Pigeon Optimization (HPS-PO) algorithm via tuning the optimal weights. In fact, the proposed algorithm is the combination of Particle Swarm Optimization (PSO) and Pigeon Optimization Algorithm (POA), respectively. Finally, the performance of the proposed model is examined over conventional methods in terms of prediction analysis and error analysis.

SECTORAL LABOR MOBILITY AND OPTIMAL MONETARY POLICY

How should central banks optimally aggregate sectoral inflation rates in the presence of imperfect labor mobility across sectors? We study this issue in a two-sector New-Keynesian model and show that a lower degree of sectoral labor mobility, ceteris paribus, increases the optimal weight on inflation in a sector that would otherwise receive a lower weight. We analytically and numerically find that, with limited labor mobility, adjustment to asymmetric shocks cannot fully occur through the reallocation of labor, thus putting more pressure on wages, causing inefficient movements in relative prices, and creating scope for central bank’ s intervention. These findings challenge standard central banks’ practice of computing sectoral inflation weights based solely on sector size and unveil a significant role for the degree of sectoral labor mobility to play in the optimal computation. In an extended estimated model of the US economy, featuring customary frictions and shocks, the estimated inflation weights imply a decrease in welfare up to 10% relative to the case of optimal weights.

An Enhanced Weight Update Method for Simplified ARTMAP to Classify Groundwater Data

The groundwater for aquatic purposes must be assessed prior to its consumption. Huge number of conventional methods are existing for assessing the quality of groundwater. The water quality index is one of the important conventional methods to assess the groundwater quality. But the conventional methods alone are not enough to assess groundwater quality as well as classify based on its purity. In this paper, we propose an enhanced weight update method for Simplified Fuzzy Adaptive Resonance Theory model to classify the groundwater quality depending on the relative weights of the groundwater quality parameters. Finding the optimal weights is the key to achieve better accuracy of the model, most of the nonlinear models fails to exhibit good accuracy if they fail to learn the optimal weights in the learning process. The aim of the work is to find the good fit between the predicted and the actual groundwater quality grades by identifying the optimal weights of the network by the enhanced weight update method. The Simplified Fuzzy Adaptive Resonance Theory map with the enhanced weight update method performance is justified by comparing it with the Simplified Fuzzy Adaptive Resonance Theory Map. The enhanced weight update method improves the accuracy of the Simplified Fuzzy Adaptive Resonance Theory Map in classifying and predicting the groundwater quality.

Firefly Algorithm Optimized Functional Link Artificial Neural Network for ISA-Radar Image Recognition

Traditional neural networks are very diverse and have been used during the last decades in the fields of data classification. These networks like MLP, back propagation neural networks (BPNN) and feed forward network have shown inability to scale with problem size and with the slow convergence rate. So in order to overcome these numbers of drawbacks, the use of higher order neural networks (HONNs) becomes the solution by adding input units along with a stronger functioning of other neural units in the network and transforms easily these input units to hidden layers. In this paper, a new metaheuristic method, Firefly (FFA), is applied to calculate the optimal weights of the Functional Link Artificial Neural Network (FLANN) by using the flashing behavior of fireflies in order to classify ISA-Radar target. The average classification result of FLANN-FFA which reached 96% shows the efficiency of the process compared to other tested methods.