TY - JOUR A1 - Panzer, Marcel A1 - Bender, Benedict A1 - Gronau, Norbert T1 - Neural agent-based production planning and control BT - an architectural review JF - Journal of Manufacturing Systems N2 - Nowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combination with conventional methods — leveraging performances compared to benchmarks and reducing dependence on human experience. This not only implies a more sophisticated focus on distributed production resources, but also broadening the perspective from a local to a global scale. Nevertheless, future research must further increase scalability and reproducibility to guarantee a simplified transfer of results to reality. KW - production planning and control KW - machine learning KW - neural networks KW - systematic literature review KW - taxonomy Y1 - 2022 U6 - https://doi.org/10.1016/j.jmsy.2022.10.019 SN - 0278-6125 VL - 65 SP - 743 EP - 766 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Omel'chenko, Oleh A1 - Laing, Carlo R. T1 - Collective states in a ring network of theta neurons JF - Proceedings of the Royal Society of London. Series A, Mathematical, physical and engineering sciences N2 - We consider a ring network of theta neurons with non-local homogeneous coupling. We analyse the corresponding continuum evolution equation, analytically describing all possible steady states and their stability. By considering a number of different parameter sets, we determine the typical bifurcation scenarios of the network, and put on a rigorous footing some previously observed numerical results. KW - theta neurons KW - neural networks KW - bumps Y1 - 2022 U6 - https://doi.org/10.1098/rspa.2021.0817 SN - 1364-5021 SN - 1471-2946 VL - 478 IS - 2259 PB - Royal Society CY - London ER - TY - JOUR A1 - Rabovsky, Milena A1 - McClelland, James L. T1 - Quasi-compositional mapping from form to meaning BT - a neural network-based approach to capturing neural responses during human language comprehension JF - Philosophical transactions of the Royal Society of London : B, Biological sciences N2 - We argue that natural language can be usefully described as quasi-compositional and we suggest that deep learning-based neural language models bear long-term promise to capture how language conveys meaning. We also note that a successful account of human language processing should explain both the outcome of the comprehension process and the continuous internal processes underlying this performance. These points motivate our discussion of a neural network model of sentence comprehension, the Sentence Gestalt model, which we have used to account for the N400 component of the event-related brain potential (ERP), which tracks meaning processing as it happens in real time. The model, which shares features with recent deep learning-based language models, simulates N400 amplitude as the automatic update of a probabilistic representation of the situation or event described by the sentence, corresponding to a temporal difference learning signal at the level of meaning. We suggest that this process happens relatively automatically, and that sometimes a more-controlled attention-dependent process is necessary for successful comprehension, which may be reflected in the subsequent P600 ERP component. We relate this account to current deep learning models as well as classic linguistic theory, and use it to illustrate a domain general perspective on some specific linguistic operations postulated based on compositional analyses of natural language. This article is part of the theme issue 'Towards mechanistic models of meaning composition'. KW - language KW - meaning KW - event-related brain potentials KW - neural networks KW - N400 KW - P600 Y1 - 2019 U6 - https://doi.org/10.1098/rstb.2019.0313 SN - 0962-8436 SN - 1471-2970 SN - 0080-4622 VL - 375 IS - 1791 PB - Royal Society CY - London ER - TY - JOUR A1 - Rabovsky, Milena T1 - Change in a probabilistic representation of meaning can account for N400 effects on articles BT - a neural network model JF - Neuropsychologia : an international journal in behavioural and cognitive neuroscience N2 - Increased N400 amplitudes on indefinite articles (a/an) incompatible with expected nouns have been initially taken as strong evidence for probabilistic pre-activation of phonological word forms, and recently been intensely debated because they have been difficult to replicate. Here, these effects are simulated using a neural network model of sentence comprehension that we previously used to simulate a broad range of empirical N400 effects. The model produces the effects when the cue validity of the articles concerning upcoming noun meaning in the learning environment is high, but fails to produce the effects when the cue validity of the articles is low due to adjectives presented between articles and nouns during training. These simulations provide insight into one of the factors potentially contributing to the small size of the effects in empirical studies and generate predictions for cross-linguistic differences in article induced N400 effects based on articles’ cue validity. The model accounts for article induced N400 effects without assuming pre-activation of word forms, and instead simulates these effects as the stimulus-induced change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error. KW - N400 KW - ERPs KW - prediction KW - neural networks KW - cue validity KW - meaning Y1 - 2020 U6 - https://doi.org/10.1016/j.neuropsychologia.2020.107466 SN - 0028-3932 SN - 1873-3514 VL - 143 PB - Elsevier CY - Oxford ER - TY - JOUR A1 - Hempel, Sabrina A1 - Adolphs, Julian A1 - Landwehr, Niels A1 - Willink, Dilya A1 - Janke, David A1 - Amon, Thomas T1 - Supervised machine learning to assess methane emissions of a dairy building with natural ventilation JF - Applied Sciences N2 - A reliable quantification of greenhouse gas emissions is a basis for the development of adequate mitigation measures. Protocols for emission measurements and data analysis approaches to extrapolate to accurate annual emission values are a substantial prerequisite in this context. We systematically analyzed the benefit of supervised machine learning methods to project methane emissions from a naturally ventilated cattle building with a concrete solid floor and manure scraper located in Northern Germany. We took into account approximately 40 weeks of hourly emission measurements and compared model predictions using eight regression approaches, 27 different sampling scenarios and four measures of model accuracy. Data normalization was applied based on median and quartile range. A correlation analysis was performed to evaluate the influence of individual features. This indicated only a very weak linear relation between the methane emission and features that are typically used to predict methane emission values of naturally ventilated barns. It further highlighted the added value of including day-time and squared ambient temperature as features. The error of the predicted emission values was in general below 10%. The results from Gaussian processes, ordinary multilinear regression and neural networks were least robust. More robust results were obtained with multilinear regression with regularization, support vector machines and particularly the ensemble methods gradient boosting and random forest. The latter had the added value to be rather insensitive against the normalization procedure. In the case of multilinear regression, also the removal of not significantly linearly related variables (i.e., keeping only the day-time component) led to robust modeling results. We concluded that measurement protocols with 7 days and six measurement periods can be considered sufficient to model methane emissions from the dairy barn with solid floor with manure scraper, particularly when periods are distributed over the year with a preference for transition periods. Features should be normalized according to median and quartile range and must be carefully selected depending on the modeling approach. KW - greenhouse gas KW - on-farm evaluation KW - emission factor KW - regression KW - ensemble methods KW - gradient boosting KW - random forest KW - neural networks KW - support vector machines Y1 - 2020 U6 - https://doi.org/10.3390/app10196938 SN - 2076-3417 VL - 10 IS - 19 PB - MDPI CY - Basel ER - TY - JOUR A1 - Rabovsky, Milena T1 - Change in a probabilistic representation of meaning can account for N400 effects on articles: a neural network model JF - Neuropsychologia N2 - Increased N400 amplitudes on indefinite articles (a/an) incompatible with expected nouns have been initially taken as strong evidence for probabilistic pre-activation of phonological word forms, and recently been intensely debated because they have been difficult to replicate. Here, these effects are simulated using a neural network model of sentence comprehension that we previously used to simulate a broad range of empirical N400 effects. The model produces the effects when the cue validity of the articles concerning upcoming noun meaning in the learning environment is high, but fails to produce the effects when the cue validity of the articles is low due to adjectives presented between articles and nouns during training. These simulations provide insight into one of the factors potentially contributing to the small size of the effects in empirical studies and generate predictions for cross-linguistic differences in article induced N400 effects based on articles’ cue validity. The model accounts for article induced N400 effects without assuming pre-activation of word forms, and instead simulates these effects as the stimulus-induced change in a probabilistic representation of meaning corresponding to an implicit semantic prediction error. KW - N400 KW - ERPs KW - prediction KW - neural networks KW - cue validity KW - meaning Y1 - 2019 VL - 143 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Zhelavskaya, Irina A1 - Spasojevic, M. A1 - Shprits, Yuri Y. A1 - Kurth, William S. T1 - Automated determination of electron density from electric field measurements on the Van Allen Probes spacecraft JF - Journal of geophysical research : Space physics N2 - We present the Neural-network-based Upper hybrid Resonance Determination (NURD) algorithm for automatic inference of the electron number density from plasma wave measurements made on board NASA's Van Allen Probes mission. A feedforward neural network is developed to determine the upper hybrid resonance frequency, fuhr, from electric field measurements, which is then used to calculate the electron number density. In previous missions, the plasma resonance bands were manually identified, and there have been few attempts to do robust, routine automated detections. We describe the design and implementation of the algorithm and perform an initial analysis of the resulting electron number density distribution obtained by applying NURD to 2.5 years of data collected with the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) instrumentation suite of the Van Allen Probes mission. Densities obtained by NURD are compared to those obtained by another recently developed automated technique and also to an existing empirical plasmasphere and trough density model. KW - Van Allen Probes KW - electron number density KW - neural networks Y1 - 2016 U6 - https://doi.org/10.1002/2015JA022132 SN - 2169-9380 SN - 2169-9402 VL - 121 SP - 4611 EP - 4625 PB - American Geophysical Union CY - Washington ER -