An annular ring compact microstrip antenna (ARCMA) constructed by loading a circular slot in the center of the circular patch antenna is a popular microstrip antenna due to its favorable properties. In this study, a m...
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An annular ring compact microstrip antenna (ARCMA) constructed by loading a circular slot in the center of the circular patch antenna is a popular microstrip antenna due to its favorable properties. In this study, a method based artificial neural networks (ANNs) has been firstly applied for the computing the resonant frequency of ARCMAs. Multilayered perceptron model based on feed forward back propagation ANN has been utilized, and the constructed model have been separately trained with 8 different learning algorithms to achieve the best results regarding the resonant frequency of ARCMAs at dominant mode. To this end, the resonant frequencies of 80 ARCMAs with varied dimensions and electrical parameters in accordance with UHF band covering GSM, LTE, WLAN and WiMAX applications were simulated with a robust numerical electromagnetic computational tool, IE3D (TM), which is based on method of moment. Then, ANN model was constructed with the simulation data, by using 70 ARCMAs for training and the remaining 10 for test. As the performances of the 8 learning algorithms are compared with each other, the best result is obtained with Levenberg-Marquardt algorithm. The proposed ANN model were confirmed by comparing with the suggestions reported elsewhere via measurement data published earlier in the literature, and they have further validated on an ARCMA fabricated in this study. The results achieved in this study show that ANN model learning with LM algorithm can be successfully used to compute the resonant frequency of ARCMAs without involving any sophisticated methods.
The financial market volatility has been a focus of study for experts over past decades. While stockbrokers and investors expect reliable projections of future stock indices, it instead displays unpredictable, complic...
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The financial market volatility has been a focus of study for experts over past decades. While stockbrokers and investors expect reliable projections of future stock indices, it instead displays unpredictable, complicated, and nonlinear reactions which paves path towards designing accurate prediction mechanisms for approximate stock market behaviour. Many computational intelligence methods have been used in the field of economic forecasting alongside more conventional methods. Yet, a trustworthy forecasting depends heavily on the correct selection of an efficient model and optimum set of model hyperparameters. In this regard, artificial neural networks (ANNs) specifically higher-order neural networks (HONNs) have shown notable performance in modelling the chaotic behaviour of financial data and predicting future data. Improved precision and nonlinear decision boundaries are two benefits of using HONN. They can store more data, map features beautifully with weights that are configurable on a single layer, learn quickly, and even tackle complex real-world situations. Besides, evolutionary optimization algorithms (EOA) are widely employed for adjusting HONN parameters thus, enhancing their generalization capability. HONNs are trained with several flavours of evolutionary learning algorithms to discover optimal solutions for various real-world complex engineering problems. The purpose of this article is to explore HONN and EOA based financial forecasts from the last two decades and see how well these forecasts performed in terms of making accurate and reliable financial predictions. We conducted a rigorous survey on state-of-the-art HONN and EOA based methods used for financial forecasting collected from reliable and reputed sources, analysed their applicability and performability, identified their strength and limitations, and suggested few constructive criticisms. Findings of this study may assist researchers of this field in adopting such forecasting methods appropri
We analyze the performance of top-down algorithms for decision tree learning, such as those employed by the widely used C4.5 and CART software packages. Our main result is a proof that such algorithms are boosting alg...
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We analyze the performance of top-down algorithms for decision tree learning, such as those employed by the widely used C4.5 and CART software packages. Our main result is a proof that such algorithms are boosting algorithms. By this we mean that if the functions that label the internal nodes of the decision tree can weakly approximate the unknown target function, then the top-down algorithms we study will amplify this weaks advantage to build a tree achieving any desired level of accuracy, The bounds we obtain for this amplification show an interesting dependence on the splitting criterion used by the top-down algorithm, More precisely, if the functions used to label the internal nodes have error 1/2 - gamma as approximations to the target function, then for the splitting criteria used by CART and C4.5, trees of size (1/epsilon)(O(1/gamma 2 epsilon 2)) and (1/epsilon)(O(log(1/epsilon)/gamma 2)) (respectively) suffice to drive the error below epsilon. Thus (for example), a small constant advantage over random guessing is amplified to any larger constant advantage with trees of constant size. For a new splitting criterion suggested by our analysis, the much stronger bound of (1/epsilon)(O(1/gamma 2)) which is polynomial in 1/epsilon) is obtained, which is provably optimal for decision tree algorithms, The differing bounds have a natured explanation in terms of concavity properties of the splitting criterion, The primary contribution of this work is in proving that some popular and empirically successful heuristics that are base on first principles meet the criteria of an independently motivated theoretical model. (C) 1999 Academic Press.
This paper investigates a class of multi-player discrete games where each player aims to maximize its own utility function. Each player does not know the other players' action sets, their deployed actions or the s...
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This paper investigates a class of multi-player discrete games where each player aims to maximize its own utility function. Each player does not know the other players' action sets, their deployed actions or the structures of its own or the others' utility functions. Instead, each player only knows its own deployed actions and its received utility values in recent history. We propose a reinforcement learning algorithm which converges to the set of action profiles which have maximal stochastic potential with probability one. Furthermore, an upper bound on the convergence rate is derived and is minimized when the exploration rates are restricted to p-series. The algorithm performance is verified using a case study in the smart grid. (C) 2019 Elsevier Ltd. All rights reserved.
The theories about crime and correction have their inception in the eighteenth century, highly influenced by the anthropological thoughts emerging during the age of Enlightenment. Throughout the decades, the criminolo...
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The theories about crime and correction have their inception in the eighteenth century, highly influenced by the anthropological thoughts emerging during the age of Enlightenment. Throughout the decades, the criminological studies observed their sociological essence encompassing practices from other scientific fields to explain the more contemporary questions, becoming Criminology an inherently interdisciplinary science as a result. The adoption of concepts from Exact Sciences is a recent moving, originating it a novel research area, called Computational Criminology, which employs procedures from Applied Mathematics, Statistics and Computer Science to provide original or enhanced solutions to such questions. One of the most prominent tasks brought by this rising field is crime prediction, which attempts to uncover potential targets for future police intervention and also help solving already committed offenses. The present comparative analysis thus investigates the employment of statistical inference by means of Bayesian network for predictive policing, using the openly accessible registers from Chicago Police Department. Numerous algorithms are available to learn the structure for a Bayesian network purely from data and a comparative examination about them is hence described, with the purpose to establish the most precise and efficient one, according to the attributes of the said criminal dataset, for the implementation of the intended inference.
We present a routing algorithm for use within a network level protocol such as ISO's routing protocols. The viability of learning automata routing in message switched and packet switched networks and the theoretic...
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We present a routing algorithm for use within a network level protocol such as ISO's routing protocols. The viability of learning automata routing in message switched and packet switched networks and the theoretical basis for analyzing such communications systems are the subject of this paper. The behavior of the automata at the nodes is studied using well-defined and mathematically tractable abstract representations of the network. Both the equilibrium and transient performance of automata operating in these environments are considered. The behavior predicted by analysis of the abstract environments is verified by simulation of automata operating in simple data communication networks. Simulation studies of our algorithm and optimum adaptive algorithms have been considered.
learning algorithms have extensively been applied to classification and pattern recognition problems in the past years. Some papers have addressed special attention to applications regarding damage assessment, especia...
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learning algorithms have extensively been applied to classification and pattern recognition problems in the past years. Some papers have addressed special attention to applications regarding damage assessment, especially how these algorithms could be used to classify different structural conditions. Nevertheless, few works present techniques in which vibration signatures can be directly used to provide insights about possible modification processes. This paper proposes a novel approach in which the concept of Symbolic Data Analysis (SDA) is introduced to manipulate not only vibration data (signals) but also modal properties (natural frequencies and mode shapes). These quantities (transformed into symbolic data) are combined to three well-known classification techniques: Bayesian Decision Trees, Neural Networks and Support Vector Machines. The objective is to explore the efficiency of this combined methodology. For this purpose, several numerical simulations are first performed for evaluating the probabilities of true detection (or true classification) in the presence of different damage conditions. Several noise levels are also applied to the data to attest the sensibility of each technique. Second, a set of experimental tests performed on a railway bridge in France is used to emphasize advantages and drawbacks of the proposed approach. Results show that the analysis combining the cited learning algorithms with the symbolic data concepts is efficient enough to classify and discriminate structural modifications with a high probability of true detection, either considering vibration data or modal parameters. Copyright (c) 2010 John Wiley & Sons, Ltd.
We consider a periodic-review single-product inventory system with fixed cost under censored demand. Under full demand distributional information, it is well known that the celebrated (s, S) policy is optimal. In this...
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We consider a periodic-review single-product inventory system with fixed cost under censored demand. Under full demand distributional information, it is well known that the celebrated (s, S) policy is optimal. In this paper, we assume the firm does not know the demand distribution a priori and makes adaptive inventory ordering decisions in each period based only on the past sales (a.k.a. censored demand). Our performance measure is regret, which is the cost difference between a feasible learning algorithm and the clairvoyant (full-information) benchmark. Compared with prior literature, the key difficulty of this problem lies in the loss of joint convexity of the objective function as a result of the presence of fixed cost. Wedevelop the first learning algorithm, termed the (d, S) policy, that combines the power of stochastic gradient descent, bandit controls, and simulation-based methods in a seamless and nontrivial fashion. We prove that the cumulative regret is O(log T root T), which is provably tight up to a logarithmic factor. We also develop several technical results that are of independent interest. We believe that the developed framework could be widely applied to learning other important stochastic systems with partial convexity in the objectives.
Multispectral LiDAR, characterization of completeness, and consistency of spectrum and spatial geometric data provide a new data source for land cover classification. However, how to choose the optimal features for a ...
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Multispectral LiDAR, characterization of completeness, and consistency of spectrum and spatial geometric data provide a new data source for land cover classification. However, how to choose the optimal features for a given set of land covers is an open problem for effective land cover classification. To address this problem, we propose a comparative scheme, which investigates a popular deep learning (deep Boltzmann machine, DBM) model for high-level feature representation and widely used machine learning methods for low-level feature extraction and selection [principal component analysis (PCA) and random forest (RF)] in land cover classification. The comparative study was conducted on the multispectral LiDAR point clouds, acquired by a Teledyne Optech's Titan airborne system. The deep learning-based highlevel feature representation experimental results showed that, on an ordinary personal computer or workstation, this method required larger training samples andmore computational complexity than the machine learning-based low-level feature extraction and selection methods. However, our comparative experiments demonstrated that the classification accuracies of the DBM-based method were higher than those of the RF-based and PCA-based methods using multispectral LiDAR data.
Leather footwear export plays a crucial role in the Indian economy as India is the second largest footwear producer in the world. As a commodity, it is unavoidable to emphasize its export performance by forecasting. T...
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Leather footwear export plays a crucial role in the Indian economy as India is the second largest footwear producer in the world. As a commodity, it is unavoidable to emphasize its export performance by forecasting. This paper aims to bring out an Artificial Neural Network based model to predict India's leather footwear export. Towards forecasting Leather footwear export, the dataset comprising five commodities covered under leather footwear has been taken from 1996 to 97 to 2021-22. The authors have proposed India's Leather Footwear Export -Artificial Neural Network (ILFE-ANN) model with SGD optimizer and activation functions such as Sigmoid / Logistic and Rectified linear unit (ReLU). The authors have kept null values as it is in the data than replacing them with imputation methods such as mean and median while modelling. Outliers are replaced with the mean value of the remaining data before modelling. Moreover, different learning algorithms such as Adaptive Moment (Adam), RMS Propagation (RMSProp), Stochastic Gradient Descent (SGD) and SGD with Momentum (SGDM) have been compared to choose an optimal one before being implemented in the ILFE-ANN. The vali-dation of the ILFE-ANN model has been implemented for the prediction of livestock population and compared with the Regression model. The variation percentage confirms that the proposed ILFE-ANN model performs significantly with 0.51% RMSProp, 1.68 % SGDM and 2.54 % SGD. Further, the minimum value of performance metrics MAE, MAPE and RMSE obtained are 0.4, 0.5 and 0.5 respectively for the prediction of sheep population for the year 2017. It shows that SGD performs better with the least error rate of 8% MAPE for the export of leather Commodity 64035113. Hence, the study confirms that the ANN model with SGD optimizer performs better for the prediction of India's leather footwear trade data.
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