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This paper introduces a reduced Gaussian kernel filtered-x least mean square (RGKxLMS) algorithm for a nonlinear active noise control (NANC) system. This algorithm addresses the computational and storage challenges posed by the traditional kernel (i.e., KFxLMS) algorithm. Then, we analyze the mean weight behavior and computational complexity of the RGKxLMS, demonstrating its reduced complexity compared to existing kernel filtering methods and its mean stable performance. To further enhance noise reduction, we also develop the historical error correction RGKxLMS (HECRGKxLMS) algorithm, incorporating historical error information. Finally, the effectiveness of the proposed algorithms is validated, using Lorenz chaotic noise, non-stationary noise environments, and factory noise. [ABSTRACT FROM AUTHOR]

Since its first adoption as a computational model for language learning, evidence has accumulated that Rescorla–Wagner error‐correction learning (Rescorla & Wagner, 1972) captures several aspects of language processing. Whereas previous studies have provided general support for the Rescorla–Wagner rule by using it to explain the behavior of participants across a range of tasks, we focus on testing predictions generated by the model in a controlled natural language learning task and model the data at the level of the individual learner. By adjusting the parameters of the model to fit the trial‐by‐trial behavioral choices of participants, rather than fitting a one‐for‐all model using a single set of default parameters, we show that the model accurately captures participants' choices, time latencies, and levels of response agreement. We also show that gender and working memory capacity affect the extent to which the Rescorla–Wagner model captures language learning. A one‐page Accessible Summary of this article in non‐technical language is freely available in the Supporting Information online and at https://oasis‐database.org [ABSTRACT FROM AUTHOR]

In this study, we propose an operationalization of the concept of emergence which plays a crucial role in usage-based theories of language. The abstractions linguists operate with are assumed to emerge through a process of generalization over the data language users are exposed to. Here, we use two types of computational learning algorithms that differ in how they formalize and execute generalization and, consequently, abstraction, to probe whether a type of language knowledge that resembles linguistic abstractions could emerge from exposure to raw data only. More specifically, we investigated whether a phone, undisputedly the simplest of all linguistic abstractions, could emerge from exposure to speech sounds using two computational learning processes: memory-based learning and error-correction learning (ECL). Both models were presented with a significant amount of pre-processed speech produced by one speaker. We assessed (1) the consistency or stability of what these simple models learn and (2) their ability to approximate abstract categories. Both types of models fare differently regarding these tests. We show that only ECL models can learn abstractions and that at least part of the phone inventory and its grouping into traditional types can be reliably identified from the input. [ABSTRACT FROM AUTHOR]

In automatic control design, identifying the system parameters based on an effective identification algorithm is often necessary. Given that many realistic plants are a multivariable system, such identification is considered critical. Most of the available multivariable system identification techniques are designed based on prediction error-correction learning, which produces an unsatisfactory estimation accuracy and low convergence speed, especially in the case of strong external interference. In this article, an adaptive identification scheme is proposed to achieve model recovery for a multivariable system based on a novel error correction learning framework. First, three fictitious sub-models are established by means of the hierarchical principle, in which the high computational burden of the identification approach is avoided. Second, the aforementioned identification method is proposed to recover the parameter information of each sub-model. To improve the identification performance, the identification error information contained in the system data is derived and used to establish a criterion function. According to the identification error and initial parameter error data, a novel criterion function structure relying on the regularization and punishment mechanisms is proposed. Based on this criterion function, a new adaptive error correction learning parameter update law is then deduced. A numerical example and a real-world plant are examined to verify the advantage and practicality of the presented adaptive identification scheme. [ABSTRACT FROM AUTHOR]

Elucidation of the conditions in which associative learning occurs is a critical issue in neuroscience and comparative psychology. In Pavlovian conditioning in mammals, it is thought that the discrepancy, or error, between the actual reward and the predicted reward determines whether learning occurs. This theory stems from the finding of Kamin’s blocking effect, in which after pairing of a stimulus with an unconditioned stimulus (US), conditioning of a second stimulus is blocked when the two stimuli are presented in compound and paired with the same US. Whether this theory is applicable to any species of invertebrates, however, has remained unknown. We first showed blocking and one-trial blocking of Pavlovian conditioning in the cricket Gryllus bimaculatus, which supported the Rescorla–Wagner model but not attentional theories, the major competitive error-correction learning theories to account for blocking. To match the prediction error theory, a neural circuit model was proposed, and prediction from the model was tested: the results were consistent with the Rescorla–Wagner model but not with the retrieval theory, another competitive theory to account for blocking. The findings suggest that the Rescorla–Wagner model best accounts for Pavlovian conditioning in crickets and that the basic computation rule underlying Pavlovian conditioning in crickets is the same to those suggested in mammals. Moreover, results of pharmacological studies in crickets suggested that octopamine and dopamine mediate prediction error signals in appetitive and aversive conditioning, respectively. This was in contrast to the notion that dopamine mediates appetitive prediction error signals in mammals. The functional significance and evolutionary implications of these findings are discussed.

Elucidation of the conditions in which associative learning occurs is a critical issue in neuroscience and comparative psychology. In Pavlovian conditioning in mammals, it is thought that the discrepancy, or error, between the actual reward and the predicted reward determines whether learning occurs. This theory stems from the finding of Kamin’s blocking effect, in which after pairing of a stimulus with an unconditioned stimulus (US), conditioning of a second stimulus is blocked when the two stimuli are presented in compound and paired with the same US. Whether this theory is applicable to any species of invertebrates, however, has remained unknown. We first showed blocking and one-trial blocking of Pavlovian conditioning in the cricket Gryllus bimaculatus, which supported the Rescorla–Wagner model but not attentional theories, the major competitive error-correction learning theories to account for blocking. To match the prediction error theory, a neural circuit model was proposed, and prediction from the model was tested: the results were consistent with the Rescorla–Wagner model but not with the retrieval theory, another competitive theory to account for blocking. The findings suggest that the Rescorla–Wagner model best accounts for Pavlovian conditioning in crickets and that the basic computation rule underlying Pavlovian conditioning in crickets is the same to those suggested in mammals. Moreover, results of pharmacological studies in crickets suggested that octopamine and dopamine mediate prediction error signals in appetitive and aversive conditioning, respectively. This was in contrast to the notion that dopamine mediates appetitive prediction error signals in mammals. The functional significance and evolutionary implications of these findings are discussed. [ABSTRACT FROM AUTHOR]

The exploration of thermoelectric materials is challenging considering the large materials space, combined with added exponential degrees of freedom coming from doping and the diversity of synthetic pathways. Here, historical data is incorporated, and is updated using experimental feedback by employing error-correction learning (ECL). This is achieved by learning from prior datasets and then adapting the model to differences in synthesis and characterization that are otherwise difficult to parameterize. This strategy is thus applied to discovering thermoelectric materials, where synthesis is prioritized at temperatures <300 °C. A previously unexplored chemical family of thermoelectric materials, PbSe:SnSb, is documented, finding that the best candidate in this chemical family, 2 wt% SnSb doped PbSe, exhibits a power factor more than 2× that of PbSe. The investigations herein reveal that a closed-loop experimentation strategy reduces the required number of experiments to find an optimized material by a factor as high as 3× compared to high-throughput searches powered by state-of-the-art machine-learning (ML) models. It is also observed that this improvement is dependent on the accuracy of the ML model in a manner that exhibits diminishing returns: once a certain accuracy is reached, factors that are instead associated with experimental pathways begin to dominate trends., (© 2023 The Authors. Advanced Materials published by Wiley-VCH GmbH.)

In this paper, we observe some important aspects of Hebbian and error-correction learning rules for complex-valued neurons. These learning rules, which were previously considered for the multi-valued neuron (MVN) whose inputs and output are located on the unit circle, are generalized for a complex-valued neuron whose inputs and output are arbitrary complex numbers. The Hebbian learning rule is also considered for the MVN with a periodic activation function. It is experimentally shown that Hebbian weights, even if they still cannot implement an input/output mapping to be learned, are better starting weights for the error-correction learning, which converges faster starting from the Hebbian weights rather than from the random ones. [ABSTRACT FROM AUTHOR]

We show that the conventional first-order algorithm of unification can be simulated by finite artificial neural networks with one layer of neurons. In these unification neural networks, the unification algorithm is performed by error-correction learning. Each time-step of adaptation of the network corresponds to a single iteration of the unification algorithm. We present this result together with the library of learning functions and examples fully formalised in MATLAB Neural Network Toolbox. [ABSTRACT FROM PUBLISHER]