Multi-Layer Neural Networks (MLNNs) have been known to be used to model the statistical properties of their training data. Several authors have shown that, depending on the objective function chosen, MLNNs estimate th...
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Multi-Layer Neural Networks (MLNNs) have been known to be used to model the statistical properties of their training data. Several authors have shown that, depending on the objective function chosen, MLNNs estimate the posterior class probabilities of their inputs, provided the network is trained with binary desired outputs. If has recently been shown that conditions exist that define a general class of objective functions which provide probability estimates. This paper introduces a method of generating such objective functions. This generator is simple to use, and so far has been found to be universally applicable. Known objective functions, which include the mean-squared error (MSE) and the cross entropy (CE) measure, are generated here as examples of its application. To demonstrate the potential of this method a new objective function is derived and discussed. This work provides practising engineers with an explicit method for generating objective functions that could be used in their classification applications. Copyright (C) 1996 Elsevier Science Ltd
Runoff prediction is a crucial aspect of water resource management and risk mitigation. Despite hydrological modelling plays a vital role in accurately representing catchment behaviour, calibration still poses a signi...
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Runoff prediction is a crucial aspect of water resource management and risk mitigation. Despite hydrological modelling plays a vital role in accurately representing catchment behaviour, calibration still poses a significant challenge. This study explores the use of knowable moments (KMoments), a category of high -order statistical moments, as part of the core of objective functions in hydrological model calibration. Traditional objective functions, such as Nash -Sutcliffe Efficiency (NSE) and Kling -Gupta Efficiency (KGE), often make assumptions about data distribution and are sensitive to outliers. KMoments offer a promising alternative by enabling reliable estimation and effective description of high -order statistics from typical hydrological samples and therefore, reducing uncertainty in their estimation and computation of the objective functions in question. Three daily lumped hydrological models (GR4J, VIC, and HYMOD) were employed to test the performance of different calibration strategies using KMoments-based objective functions and compare them with conventional approaches. The hydrological consistency of the simulations was also assessed through 27 hydrological signatures. Our findings highlight the advantages of using KMoments, including improved performance metrics and enhanced hydrological signature reproduction. The findings contribute to advancing hydrological modelling techniques and provide valuable insights for researchers and practitioners seeking to enhance simulation accuracy and reliability.
Two objective functions for multi-element optimization in ICP-AES were compared using signal-to-background ratios as a figure of merit. Complete three-dimensional response surfaces were generated for a number of eleme...
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Two objective functions for multi-element optimization in ICP-AES were compared using signal-to-background ratios as a figure of merit. Complete three-dimensional response surfaces were generated for a number of elements (Ca, Cu, Al, Na, Ni, Mn and Ba) and two artificial 'elements' to evaluate the performance of both objective functions in locating the optimum compromise instrumental operating conditions in multi-element determinations, In the determination of the best compromise instrument operating conditions for most combinations of the elements used, both objective functions performed equally well;however, one occasionally performed significantly better than the other.
Traditional hydrological objective functions may penalize models that reproduce hydrograph shapes well, but with some shift in time;especially for urban catchments with a fast hydrological response. Hydrograph timing ...
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Traditional hydrological objective functions may penalize models that reproduce hydrograph shapes well, but with some shift in time;especially for urban catchments with a fast hydrological response. Hydrograph timing is not always critical, so this paper investigates alternative objective functions (based on the Hydrograph Matching Algorithm) that try to mimic visual hydrograph comparison. A modified version of the Generalized Likelihood Uncertainty Estimation is proposed to compare regular objective functions with those that account for timing errors. This is applied to 2-year calibration and validation data sets from an urban catchment. Results show that such objective functions provide equally reliable model predictions (they envelop the same fraction of observations), but with more precision, i.e. smaller estimated uncertainty of model predictions. Additionally, identifiability of some model parameters improved. Therefore objective functions based on the Hydrograph Matching Algorithm can be useful to reduce uncertainties in urban drainage modelling.
In the optimum coordination of Directional Overcurrent Relays (DOCRs), operational time of relays is minimized with maintaining coordination criteria between Primary and Backup (P/B) relay pairs. In the literature, a ...
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ISBN:
(纸本)9781538617892
In the optimum coordination of Directional Overcurrent Relays (DOCRs), operational time of relays is minimized with maintaining coordination criteria between Primary and Backup (P/B) relay pairs. In the literature, a range of objective functions (OFs) including constraints with various weightings are developed to obtain the optimum coordination of relays. In this paper, the performance of different OFs is evaluated on IEEE 30-bus system using Sequential quadratic Programming (SQP). The comparative analysis is executed to find the best OF for the coordination problem of DOCRs.
The IPv6 Routing Protocol for Low Power Lossy Networks (RPL) is one of the standardized routing protocols for lossy networks consisting of resource-constrained Internet of Things (IoT) devices. RPL allows to use diffe...
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ISBN:
(纸本)9789897585517
The IPv6 Routing Protocol for Low Power Lossy Networks (RPL) is one of the standardized routing protocols for lossy networks consisting of resource-constrained Internet of Things (IoT) devices. RPL allows to use different objective functions based on different routing metrics such as expected transmission count (ETX), hop count, and energy to determine effective routes. In the literature, the performance of two objective functions namely objective Function Zero (OF0), Minimum Rank with Hysteresis objective Function (MRHOF) are evaluated thoroughly, since they are accepted as standard objective functions in RPL. However their performance under attack has not been evaluated comprehensively yet. Although RPL has defined some specifications for its security, it is still vulnerable to insider attacks, which could dramatically affect the network performance. Therefore, this study investigates how the performance of objective functions are affected by RPL specific attacks. Version number, DIS flooding, and worst parent attacks are analyzed by using the following performance metrics: packet delivery ratio, overhead, latency, and power consumption. Moreover, how they are affected by the number of attackers in the network are analyzed. To the best of the authors' knowledge, this is the first study that comprehensively explores RPL objective functions on networks under attacks.
We examine the effects of the choice of neural network objective (criterion) functions on the ability of the neural network to perform detection. The experiments are performed using a multilayer perceptron with the me...
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ISBN:
(纸本)0780341732
We examine the effects of the choice of neural network objective (criterion) functions on the ability of the neural network to perform detection. The experiments are performed using a multilayer perceptron with the mean square error, classification figure of merit (CFM), maximally hat CFM and the modified perceptron error objective functions. We develop a thresholding scheme for the outputs of the neural network in order to obtain receiver operating characteristic (ROC) curves for the various objective functions. We perform preliminary tests on a breast cancer cell detection problem.
Audio-visual speech enhancement (AV-SE) is the task of improving speech quality and intelligibility in a noisy environment using audio and visual information from a talker. Recently, deep learning techniques have been...
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ISBN:
(纸本)9781479981311
Audio-visual speech enhancement (AV-SE) is the task of improving speech quality and intelligibility in a noisy environment using audio and visual information from a talker. Recently, deep learning techniques have been adopted to solve the AV-SE task in a supervised manner. In this context, the choice of the target, i.e. the quantity to be estimated, and the objective function, which quantifies the quality of this estimate, to be used for training is critical for the performance. This work is the first that presents an experimental study of a range of different targets and objective functions used to train a deep-learning-based AV-SE system. The results show that the approaches that directly estimate a mask perform the best overall in terms of estimated speech quality and intelligibility, although the model that directly estimates the log magnitude spectrum performs as good in terms of estimated speech quality.
Although Japanese credit associations are non-profit cooperative financial institutions, they assume the same financial functions as regional banks that are stock companies and they could compete with each other in a ...
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Although Japanese credit associations are non-profit cooperative financial institutions, they assume the same financial functions as regional banks that are stock companies and they could compete with each other in a regional market. On the other hand, the governance structures of credit associations tend to exhibit weaker discipline than those of regional banks, and, for this reason, the financial performances of credit associations and regional banks might differ. In this article, we empirically investigated whether the objective functions of credit associations are different from those of regional banks considering their different governance structures. As a result, although significant differences of profitability of these two types of institutions were not detected, it was demonstrated that credit associations can capture a greater share of deposits than regional banks and the former are more conservative in risk taking than the latter. From these, there is a possibility that Japanese credit associations have different objective functions from regional banks.
The predominant view of the role of business in society is that the objective of business is to maximize profit. Some argue that it ought to be something different. Others argue that for many firms it already is somet...
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The predominant view of the role of business in society is that the objective of business is to maximize profit. Some argue that it ought to be something different. Others argue that for many firms it already is something different. However, the something different has not been fully fleshed out in its various versions. To address this gap, we define different relationship types between variables in an objective function and develop and present the resulting range of 10 alternative objective functions for firms. We then discuss how their development contributes to conceptual, empirical, and normative debates about organizational purpose. Removing the conventional assumption of profit maximization as the sole management principle opens up the possibility of new, more nuanced theoretical approaches to management. This article lays the groundwork for such theory development through the systematic and analytical identification of alternative objective functions that represent different specifications of firm purpose.
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