In this article, a new adaptive fuzzy wavelet neural network model is proposed for nonlinear function approximation problems. The AFWNN model is based on the traditional Takagi-Sugeno-Kang fuzzy system. Specifically, this model replaces the membership functions of fuzzy rules with wavelet basis functions, which are known to have time and frequency localization properties, i.e., they can approximate patterns both in the time and frequency domains. The structure of the AFWNN model is derived from that of the adaptive neuro-fuzzy (...) inference system. However, the AFWNN improves over the ANFIS by replacing Gaussian functions in the hidden layer with wavelet basis functions. The AFWNN model is trained using a gradient-based optimization algorithm. The AFWNN is then tested on three function approximation problems of time series prediction. For certain types of nonlinear time series, for instance fractal processes, the AFWNN is found to be substantially more accurate than alternative methods. (shrink)

Cognition or higher brain activity is sometimes seen as a phenomenon greater than the sum of its parts. This viewpoint however is largely dependent on the state of the art of experimental techniques that endeavor to characterize morphology and its association to function. Retinal ganglion cells are readily accessible for this work and we discuss recent advances in computational techniques in identifying novel parameters that describe structural attributes possibly associated with specific function. These parameters are based on calculating wavelet (...) gradients from cell images followed by the extraction of meaningful measures including 2nd wavelet moment, entropy of orientation, and curvature. For the three cell types analyzed, the mean 2nd wavelet moment, which relates to the field of influence of the dendritic-tree segments was significantly different. cells had the highest mean 2nd wavelet moment, followed by the and cells (134 ± 22, 93 ± 19 and 63 ± 12, respectively). There was no significant difference between cells for entropy of orientation, indicating no class with a preferential orientation of their dendritic tree. Curvature provided similar results to the 2nd wavelet moment with cells having the highest curvature followed by and the cells (mean ± SD: 161 ± 15; 134 ± 22; 121 ± 15). Our feature space analysis also indicated a difference between these cell types. No difference was found between the and cell types and their physiological counterparts the Y and X cells based on wavelet analysis. Both the X and Y cells can be divided into two subtypes, the ON- and OFF-center cells based on the stratification level of the dendritic tree within the retina. Using 2nd wavelet moment, a difference in their morphological attributes, not reported previously, was noted for these subtypes. The 2nd wavelet moment and curvature are further discussed with respect to explaining regularity of spacing and coverage associated with retinal ganglion cell mosaics. (shrink)

Water quality prediction is the basis of water environmental planning, evaluation, and management. In this work, a novel intelligent prediction model based on the fuzzy wavelet neural network including the neural network, the fuzzy logic, the wavelet transform, and the genetic algorithm was proposed to simulate the nonlinearity of water quality parameters and water quality predictions. A self-adapted fuzzy c-means clustering was used to determine the number of fuzzy rules. A hybrid learning algorithm based on a genetic algorithm (...) and gradient descent algorithm was employed to optimize the network parameters. Comparisons were made between the proposed FWNN model and the fuzzy neural network, the wavelet neural network, and the neural network. The results indicate that the FWNN made effective use of the self-adaptability of NN, the uncertainty capacity of FL, and the partial analysis ability of WT, so it could handle the fluctuation and the nonseasonal time series data of water quality, while exhibiting higher estimation accuracy and better robustness and achieving better performances for predicting water quality with high determination coefficients R2 over 0.90. The FWNN is feasible and reliable for simulating and predicting water quality in river. (shrink)

A new medical image enhancement algorithm based on spatial frequency domain is presented in this article. The medical image is first divided into several sub-images based on dyadic wavelet scale analysis. At each level, different directional sub-band images can reflect the different characteristics of the image. A low-frequency sub-band image maintains the original image content information, and high-frequency sub-band images represent image details such as edges and regional boundaries. The corresponding sub-band images are then enhanced by different Butterworth homomorphic (...) filtering functions, which can attenuate the low frequencies and amplify the high frequencies. A linear adjustment is carried out on the low frequency of the highest level. Then, the wavelet reconstruction course is used to obtain the final enhanced image. Experiments on magnetic resonance images of temporomandibular joint soft tissues have shown that the proposed method can effectively eliminate the non-uniform luminance distribution of medical images. Its performance is much better than traditional Butterworth homomorphic filtering algorithm whether in subjective vision quality or objective evaluations such as detailed information entropy and average gradient. (shrink)

Comment Bachelard, fils d'un cordonnier, professeur de physique et chimie, a-t-il pu devenir cet humaniste aussi savant que philosophe, aussi penseur que poète? Il n'a pas emprunté les chemins balisés, ceux des élites universitaires et culturelles. Il a contrarié les pronostics et les conventions. Il s'est adjugé contre vents et marées le droit de penser par lui-même en bousculant les frontières des savoirs et de la culture et en dérangeant les us et coutumes établis. "Un ouvrage aussi lumineux que la (...) destinée et l'oeuvre de Gaston Bachelard. Au plus près de sa vie et de sa pensée". (Philippe Meirieu). (shrink)

Accurate short-term traffic flow modeling is an essential prerequisite to analyze and control traffic flow. Canonical data-driven methods are a large account of parameters that may be underfitted with limited training samples, yet they cannot adaptively boost their understanding of the spatiotemporal dependencies of the traffic flow. The noisy and unstable traffic flow data also prevent the models from effectively learning the underlying patterns for forecasting future traffic flow. To address these issues, we propose an easy-to-implement yet effective boosting model (...) based on extreme gradient boosting and enhance it by wavelet denoising for short-term traffic flow forecasting. The discrete wavelet denoising is employed to preprocess the noisy traffic flow data. Then, the denoised training datasets are reconstructed to train the extreme gradient boosting model. These two components are integrated seamlessly in a unified framework, and the whole framework can retain the features in the data as much as possible. Our model can precisely capture the hidden spatial dependency in the data. Extensive experiments are conducted on four benchmark datasets compared with frequently used models. The results demonstrate that the proposed model can precisely capture the hidden spatial dependency of the traffic flow data and achieve superior performance. (shrink)

Chemiosmotic coupling − the harnessing of electrochemical ion gradients across membranes to drive metabolism − is as universally conserved as the genetic code. As argued previously in these pages, such deep conservation suggests that ion gradients arose early in evolution, and might have played a role in the origin of life. Alkaline hydrothermal vents harbour pH gradients of similar polarity and magnitude to those employed by modern cells, one of many properties that make them attractive models for life's origin. Their (...) congruence with the physiology of anaerobic autotrophs that use the acetyl CoA pathway to fix CO2 gives the alkaline vent model broad appeal to biologists. Recently, however, a paper by Baz Jackson criticized the hypothesis, concluding that natural pH gradients were unlikely to have played any role in the origin of life. Unfortunately, Jackson mainly criticized his own interpretations of the theory, not what the literature says. This counterpoint is intended to set the record straight. (shrink)

The concept of wild food does not play a significant role in contemporary nutritional science and it is seldom regarded as a salient feature within standard dietary guidelines. The knowledge systems of wild edible taxa are indeed at risk of disappearing. However, recent scholarship in ethnobotany, field biology, and philosophy demonstrated the crucial role of wild foods for food biodiversity and food security. The knowledge of how to use and consume wild foods is not only a means to deliver high-end (...) culinary offerings, but also a way to foster alternative models of consumption. Our aim in this paper is to provide a conceptual framework for wild foods, which can account for diversified wild food ontologies. In the first section of the paper, we survey the main conception of wild foods provided in the literature, what we call the Nature View. We argue that this view falls short of capturing characteristics that are core to a sound account of wilderness in a culinary sense. In the second part of the paper, we provide the foundation for an improved model of wild food, which can countenance multiple dimensions and degrees connoting wilderness in the culinary world. In the third part of the paper we argue that thanks to a more nuanced ontological analysis, the gradient framework can serve ethnobiologists, philosophers, scientists, and policymakers to represent and negotiate theoretical conflicts on the nature of wild food. (shrink)

The two parameters of leaf dimension namely, length and width, show inverse correlation with the third parameter, the thickness. A thermal diffusion model is proposed which explains the inverse relationship between these and envisages that while leaf length and width are directly influenced by the microclimate the thickness is affected by the microclimate through endoclimate and energy balance in the leaves. The significance of the model is discussed in the light of its importance in assessing the survival range of plant (...) species along the altitudinal gradient. (shrink)

For the safe and economical construction of embankment dams, the mechanical behaviour of the rockfill materials used in the dam’s shell must be analyzed. The characterization of rockfill materials with specified shear strength is difficult and expensive due to the presence of particles greater than 500 mm in diameter. This work investigates the feasibility of using an extreme gradient boosting computing paradigm to estimate the shear strength of rockfill materials. To train and validate the proposed XGBoost model, a total (...) of 165 databases obtained from the literature are chosen. The XGBoost model was compared against support vector machine, adaptive boosting, random forest, and K-nearest neighbor models described in the literature. XGBoost beats SVM, RF, AdaBoost, and KNN models in terms of performance evaluation metrics such as coefficient of determination, Nash–Sutcliffe coefficient, and error in the root mean square ratio to the standard deviation of the measured data. The results demonstrated that the XGBoost model has the highest prediction performance with, followed by the SVM model with, RF, the AdaBoost model with and the KNN model with. A sensitivity analysis has been conducted to ascertain the impact of each investigated input parameter. This study demonstrates that the established XGBoost model for estimating the shear strength of rockfill materials is reliable. (shrink)

Recent decades have witnessed the rapid evolution of robotic applications and their expansion into a variety of spheres with remarkable achievements. This article researches a crucial technique of robot manipulators referred to as visual servoing, which relies on the visual feedback to respond to the external information. In this regard, the visual servoing issue is tactfully transformed into a quadratic programming problem with equality and inequality constraints. Differing from the traditional methods, a gradient-based recurrent neural network for solving the (...) visual servoing issue is newly proposed in this article in the light of the gradient descent method. Then, the stability proof is presented in theory with the pixel error convergent exponentially to zero. Specifically speaking, the proposed method is able to impel the manipulator to approach the desired static point while maintaining physical constraints considered. After that, the feasibility and superiority of the proposed GRNN are verified by simulative experiments. Significantly, the proposed visual servo method can be leveraged to medical robots and rehabilitation robots to further assist doctors in treating patients remotely. (shrink)

Mental representations have continuous as well as discrete, combinatorial properties. For example, while predominantly discrete, phonological representations also vary continuously; this is reflected by gradient effects in instrumental studies of speech production. Can an integrated theoretical framework address both aspects of structure? The framework we introduce here, Gradient Symbol Processing, characterizes the emergence of grammatical macrostructure from the Parallel Distributed Processing microstructure (McClelland, Rumelhart, & The PDP Research Group, 1986) of language processing. The mental representations that emerge, Distributed (...) Symbol Systems, have both combinatorial and gradient structure. They are processed through Subsymbolic Optimization–Quantization, in which an optimization process favoring representations that satisfy well-formedness constraints operates in parallel with a distributed quantization process favoring discrete symbolic structures. We apply a particular instantiation of this framework, λ-Diffusion Theory, to phonological production. Simulations of the resulting model suggest that Gradient Symbol Processing offers a way to unify accounts of grammatical competence with both discrete and continuous patterns in language performance. (shrink)

There are stable wavelets which satisfy the Schrödinger equation. The motion of a wavelet is determined by a set of ordinary differential equations. In a certain limit, a wavelet turns out to be the known representation of a classical material point. A de Broglie wave is constructed by superposing similar free wavelets. Conventional energy eigensolutions of the Schrödinger equation can be interpreted as ensembles of wavelets. If the dynamics of wavelets form the quantum mechanical counterpart of Newton's dynamics (...) of particles, then conventional quantum mechanics is the counterpart of Gibbs's mechanics of ensembles. In this way, conventional quantum mechanics is reinterpreted on a deterministic basis. A difficulty of quantum field theory is predictable from this point of view. (shrink)