Learning, Generalization, and Functional Entropy in Random Automata Networks

It has been shown \citep{broeck90:physicalreview,patarnello87:europhys} that feedforward Boolean networks can learn to perform specific simple tasks and generalize well if only a subset of the learning examples is provided for learning. Here, we extend this body of work and show experimentally that random Boolean networks (RBNs), where both the interconnections and the Boolean transfer functions are chosen at random initially, can be evolved by using a state-topology evolution to solve simple tasks. We measure the learning and generalization performance, investigate the influence of the average node connectivity $K$, the system size $N$, and introduce a new measure that allows to better describe the network's learning and generalization behavior. We show that the connectivity of the maximum entropy networks scales as a power-law of the system size $N$. Our results show that networks with higher average connectivity $K$ (supercritical) achieve higher memorization and partial generalization. However, near critical connectivity, the networks show a higher perfect generalization on the even-odd task

Replica theory for learning curves for Gaussian processes on random graphs

Statistical physics approaches can be used to derive accurate predictions for the performance of inference methods learning from potentially noisy data, as quantified by the learning curve defined as the average error versus number of training examples. We analyse a challenging problem in the area of non-parametric inference where an effectively infinite number of parameters has to be learned, specifically Gaussian process regression. When the inputs are vertices on a random graph and the outputs noisy function values, we show that replica techniques can be used to obtain exact performance predictions in the limit of large graphs. The covariance of the Gaussian process prior is defined by a random walk kernel, the discrete analogue of squared exponential kernels on continuous spaces. Conventionally this kernel is normalised only globally, so that the prior variance can differ between vertices; as a more principled alternative we consider local normalisation, where the prior variance is uniform

Self Normalizing Flows

Efficient gradient computation of the Jacobian determinant term is a core problem in many machine learning settings, and especially so in the normalizing flow framework. Most proposed flow models therefore either restrict to a function class with easy evaluation of the Jacobian determinant, or an efficient estimator thereof. However, these restrictions limit the performance of such density models, frequently requiring significant depth to reach desired performance levels. In this work, we propose Self Normalizing Flows, a flexible framework for training normalizing flows by replacing expensive terms in the gradient by learned approximate inverses at each layer. This reduces the computational complexity of each layer's exact update from $\mathcal{O}(D^3)$ to $\mathcal{O}(D^2)$, allowing for the training of flow architectures which were otherwise computationally infeasible, while also providing efficient sampling. We show experimentally that such models are remarkably stable and optimize to similar data likelihood values as their exact gradient counterparts, while training more quickly and surpassing the performance of functionally constrained counterparts

Estimation of soluble solids content and fruit temperature in 'rocha' pear using Vis-NIR spectroscopy and the spectraNet–32 deep learning architecture

Spectra-based methods are becoming increasingly important in Precision Agriculture as they offer non-destructive, quick tools for measuring the quality of produce. This study introduces a novel approach for esti-mating the soluble solids content (SSC) of 'Rocha' pears using the SpectraNet-32 deep learning architecture, which operates on 1D fruit spectra in the visible to near-infrared region (Vis-NIRS). This method was also able to estimate fruit temperatures, which improved the SSC prediction performance. The dataset consisted of 3300 spectra from 1650 'Rocha' pears collected from local markets over several weeks during the 2010 and 2011 seasons, which had varying edaphoclimatic conditions. Two types of partial least squares (PLS) feature selection methods, under various configurations, were applied to the input spectra to identify the most significant wavelengths for training SpectraNet-32. The model's robustness was also compared to a similar state-of-the-art deep learning architecture, DeepSpectra, as well as four other classical machine learning algorithms: PLS, multiple linear regression (MLR), support vector machine (SVM), and multi-layer perceptron (MLP). In total, 23 different experimental method configurations were assessed, with 150 neural networks each. SpectraNet-32 consistently outperformed other methods in several metrics. On average, it was 6.1% better than PLS in terms of the root mean square error of prediction (RMSEP, 1.08 vs. 1.15%), 7.7% better in prediction gain (PG, 1.67 vs. 1.55), 3.6% better in the coefficient of determination (R2, 0.58 vs. 0.56) and 5.8% better in the coefficient of variation (CV%, 8.35 vs. 8.86).info:eu-repo/semantics/publishedVersio

Nonholonomic Mobile Robot Trajectory Tracking using Hybrid Controller

ABSTRACT A control scheme is being presented for the trajectory tracking of a nonholonomic kinematic model of mobile robots. As a kinematic model of mobile robots is nonlinear in nature, therefore, it is controlling is always being a difficult task. Thus, a control hybrid scheme comprises of fuzzy logic and PID (Proportional Integral Derivative) is being proposed, in which adaptive gains of PID controller is being tuned by a fuzzy logic controller. Moreover, the effectiveness of this innovative technique is also proved using the simulations by adding model uncertainties and external disturbances in the system. Besides, the fuzzy logic control system is also being compared by the proposed control system. Resultsattained shows that the fuzzy based PID controller drivesimproved results than fuzzy logic controller

MODELO NEURO-FUZZY DE APOIO À DECISÃO BASEADO NOS CLIENTES EM SHOPPING CENTER

Este estudo apresenta o desenvolvimento de um modelo matemático de apoio à decisão, baseado em atributos relevantes à percepção dos clientes de um Shopping Center. Abordam-se os aspectos relacionados à qualidade do shopping quanto ao bem-estar, lazer e consumo. Com foco exploratório, em uma pesquisa de natureza aplicada, apoiou-se na revisão bibliográfica para identificação dos atributos vinculados com a experiência do cliente em Shopping Center, seguindo-se com abordagem qualitativa aplicada em estudo de caso. Para tal necessitou-se entrevistar 100 pessoas para coleta de dados sobre os atributos. A saída do modelo permite refletir o consenso da opinião dos clientes por intermédio de indicadores gerenciais. Sendo o principal o IPC, que resultou em 6,90 para [0,10], sendo zero a pior nota. Embora oresultado do IPC evidencie não superação às expectativas dos clientes, osserviços outros indicadores, em sua maioria, caracterizam melhoria, mas com margem de aperfeiçoamento

Simulation d'un réseau de neurones à l'aide de transistors SET

Ce mémoire est le résultat d'une recherche purement exploratoire concernant la définition d'une application de réseaux de neurones à base de transistors monoélectroniques (Single-Electron Transistor, SET). Il dresse un portait de l'état de l'art actuel, et met de l'avant la possibilité d'associer les SET avec la technologie actuelle (Field Electron Transistor, FET). La raison de cette association est que les SET peuvent être perçus comme un moyen de changement de paradigme, c'est-à-dire remplacer une fonction CMOS occupant une grande place par un dispositif alternatif présentant de meilleures performances ou équivalentes. Par l'intermédiaire de leurs caractéristiques électriques peu ordinaires au synonyme de"l'effet de blocage de Coulomb", les SET ont le potentiel d'être exploités intelligemment afin de tirer profit sur la consommation énergétique essentiellement. Cette problématique est présentée comme une des propositions alternatives"Beyond CMOS" aux termes de la diminution géométrique des transistors FET à la lumière de l'ITRS. Cette recherche propose d'exposer des circuits électroniques de technologie MOS complétés à l'aide de SET (circuits hybrides) et de montrer que l'on est capable de les remplacer ou les compléter (partiellement) dans des architectures à réseau de neurones. Pour cela, des simulations sous logiciel Cadence Environnement permettront de valider le comportement des circuits sur plusieurs critères tels que la vitesse de réponse et la consommation énergétique, par exemple. En résultat, seront proposées deux architectures à réseaux de neurones de fonctions différentes : une architecture Winner-Take-All et un générateur de spikes en tension. La première étant inspirée d'une publication provenant de GUIMARAES et al., veut démontrer qu'à partir d'une architecture SET existante, il est envisageable de se l'approprier et de l'appliquer aux paramètres des SET du CRN[indice supérieur 2] augmentant donc nos chances de pouvoir les concevoir dans notre groupe de recherche. Le second axe est la simulation d'un circuit capable de générer des signaux à spikes sans perte d'information, ce qui requerrait un nombre considérable de transistors FET sans l'utilisation de SET, mettant donc en valeur la réduction de composants