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Academic literature on the topic 'Cascade neural networks'

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Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Cascade neural networks"

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Shpinareva, Irina M., Anastasia A. Yakushina, Lyudmila A. Voloshchuk, and Nikolay D. Rudnichenko. "Detection and classification of network attacks using the deep neural network cascade." Herald of Advanced Information Technology 4, no. 3 (October 15, 2021): 244–54. http://dx.doi.org/10.15276/hait.03.2021.4.

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This article shows the relevance of developing a cascade of deep neural networks for detecting and classifying network attacks based on an analysis of the practical use of network intrusion detection systems to protect local computer networks. A cascade of deep neural networks consists of two elements. The first network is a hybrid deep neural network that contains convolutional neural network layers and long short-term memory layers to detect attacks. The second network is a CNN convolutional neural network for classifying the most popular classes of network attacks such as Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnais-sance, Shellcode, and Worms. At the stage of tuning and training the cascade of deep neural networks, the selection of hyperparame-ters was carried out, which made it possible to improve the quality of the model. Among the available public datasets, one ofthe current UNSW-NB15 datasets was selected, taking into account modern traffic. For the data set under consideration, a data prepro-cessing technology has been developed. The cascade of deep neural networks was trained, tested, and validated on the UNSW-NB15 dataset. The cascade of deep neural networks was tested on real network traffic, which showed its ability to detect and classify at-tacks in a computer network. The use of a cascade of deep neural networks, consisting of a hybrid neural network CNN + LSTM and a neural network CNNhas improved the accuracy of detecting and classifying attacks in computer networks and reduced the fre-quency of false alarms in detecting network attacks
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Pedrycz, W., M. Reformat, and C. W. Han. "Cascade Architectures of Fuzzy Neural Networks." Fuzzy Optimization and Decision Making 3, no. 1 (March 2004): 5–37. http://dx.doi.org/10.1023/b:fodm.0000013070.26870.e6.

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Konarev, D. I., and A. A. Gulamov. "Synthesis of Neural Network Architecture for Recognition of Sea-Going Ship Images." Proceedings of the Southwest State University 24, no. 1 (June 23, 2020): 130–43. http://dx.doi.org/10.21869/2223-1560-2020-24-1-130-143.

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Purpose of research. The current task is to monitor ships using video surveillance cameras installed along the canal. It is important for information communication support for navigation of the Moscow Canal. The main subtask is direct recognition of ships in an image or video. Implementation of a neural network is perspectively.Methods. Various neural network are described. images of ships are an input data for the network. The learning sample uses CIFAR-10 dataset. The network is built and trained by using Keras and TensorFlow machine learning libraries.Results. Implementation of curving artificial neural networks for problems of image recognition is described. Advantages of such architecture when working with images are also described. The selection of Python language for neural network implementation is justified. The main used libraries of machine learning, such as TensorFlow and Keras are described. An experiment has been conducted to train swirl neural networks with different architectures based on Google collaboratoty service. The effectiveness of different architectures was evaluated as a percentage of correct pattern recognition in the test sample. Conclusions have been drawn about parameters influence of screwing neural network on showing its effectiveness.Conclusion. The network with a single curl layer in each cascade showed insufficient results, so three-stage curls with two and three curl layers in each cascade were used. Feature map extension has the greatest impact on the accuracy of image recognition. The increase in cascades' number has less noticeable effect and the increase in the number of screwdriver layers in each cascade does not always have an increase in the accuracy of the neural network. During the study, a three-frame network with two buckling layers in each cascade and 128 feature maps is defined as an optimal architecture of neural network under described conditions. operability checking of architecture's part under consideration on random images of ships confirmed the correctness of optimal architecture choosing.
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Duan, Shuo, Shuai Xu, Xiao Meng Xu, Xin Zhang, and Chang Li Zhou. "Simultaneous Determination of p-Nitrochlorobenzene and o-Nitrophenol in Mixture by Single-Sweep Oscillopolarography Based on Cascade Neural Network." Advanced Materials Research 217-218 (March 2011): 1469–74. http://dx.doi.org/10.4028/www.scientific.net/amr.217-218.1469.

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By combining the improved wavelet neural network and BP neural network, a new structure based on mixed cascade neural network was established. The novel cascade neural network has been used to the oscillopolargriphic signals analysis. By the figure fitting and parameters extracting, we realized the prediction of the simulation samples.The training speed and the predication accuracy can be enhanced by optimizing the network structure and parameters. The result of concentration prediction is satisfied . The method has been applied to the simultaneous determination of p- Nitrochlorobenzene (p-NCB) and o-Nitrophenol (o-NP) in simulation samples with satisfactory results. The Relative error and Recovery of p-NCB、o-NP were 3.76%、96.2%； 4.05%、96.0%, respectively. This novel cascade neural network combines the advantage of wavelet neural networks and BP neural networks, and performs its own functions respectively. It has shown a unique advantage in the overlap peak analyze.
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KWAK, Keun-Chang. "A Development of Cascade Granular Neural Networks." IEICE Transactions on Information and Systems E94-D, no. 7 (2011): 1515–18. http://dx.doi.org/10.1587/transinf.e94.d.1515.

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Choi, S., and A. Cichocki. "Cascade neural networks for multichannel blind deconvolution." Electronics Letters 34, no. 12 (1998): 1186. http://dx.doi.org/10.1049/el:19980856.

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Smith, H. Allison, and J. Geoffrey Chase. "Identification of Structural System Parameters Using the Cascade-Correlation Neural Network." Journal of Dynamic Systems, Measurement, and Control 116, no. 4 (December 1, 1994): 790–92. http://dx.doi.org/10.1115/1.2899280.

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The use of neural networks for structural system identification is receiving an increasing amount of attention through the research focused on structural control and intelligent systems. These systems require continuous monitoring and controlling of structural response; thus, on-line identification techniques are needed to provide real-time information about structural parameters. The Cascade-Correlation (Cascor) neural network is applied here to the structural system identification problem. The Cascor network utilizes a dynamic network architecture and a variable error threshold mechanism which facilitates training and can increase the network’s ability to generalize.
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Patan, Krzysztof. "Local stability conditions for discrete-time cascade locally recurrent neural networks." International Journal of Applied Mathematics and Computer Science 20, no. 1 (March 1, 2010): 23–34. http://dx.doi.org/10.2478/v10006-010-0002-x.

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Local stability conditions for discrete-time cascade locally recurrent neural networksThe paper deals with a specific kind of discrete-time recurrent neural network designed with dynamic neuron models. Dynamics are reproduced within each single neuron, hence the network considered is a locally recurrent globally feedforward. A crucial problem with neural networks of the dynamic type is stability as well as stabilization in learning problems. The paper formulates local stability conditions for the analysed class of neural networks using Lyapunov's first method. Moreover, a stabilization problem is defined and solved as a constrained optimization task. In order to tackle this problem, a gradient projection method is adopted. The efficiency and usefulness of the proposed approach are justified by using a number of experiments.
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Shuqi Zhang, Shuqi Zhang. "Cascade Attention-based Spatial-temporal Convolutional Neural Network for Motion Image Posture Recognition." 電腦學刊 33, no. 1 (February 2022): 021–30. http://dx.doi.org/10.53106/199115992022023301003.

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<p>The traditional motion posture recognition methods cannot capture the temporal relationship in a video sequence, which leads to the problem that the recognition effect of time-dependent behaviors is not ideal. Therefore, this paper proposes a cascade attention-based spatial-temporal convolutional neural network for motion posture recognition. Firstly, the convolutional neural network is used to model the time sequence relationship in the video, so as to capture the spatial-temporal information in the video efficiently. At the same time, the cascade attention mechanism is used to improve the low learning ability of spatial features caused by channel information moving on the time axis. Meanwhile, a new spatial-temporal network structure is constructed, which includes the spatial-temporal appearance information flow and spatial-temporal motion information flow. Finally, the weighted average method is used to fuse the two spatial-temporal networks to obtain the final recognition result. Experiments are conducted on UCF101 and HMDB51 datasets, respectively, and the recognition accuracy is 96.8% and 79.6%. Experiment results show that compared with the state-of-the-art network methods, the recognition accuracy with the proposed method has better effect and robustness.</p> <p>&nbsp;</p>
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Smit, Mohammad, and Abdel-Nasser Al-Assimi. "Cascade Deep Neural Networks Classifiers for Phonemes Recognition." Journal of Engineering and Applied Sciences 15, no. 7 (March 14, 2020): 1664–70. http://dx.doi.org/10.36478/jeasci.2020.1664.1670.

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Dissertations / Theses on the topic "Cascade neural networks"

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Obiegbu, Chigozie. "Image compression using cascaded neural networks." ScholarWorks@UNO, 2003. http://louisdl.louislibraries.org/u?/NOD,51.

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Thesis (M.S.)--University of New Orleans, 2003.
Title from electronic submission form. "A thesis ... in partial fulfillment of the requirements for the degree of Master of Science in the Department of Electrical Engineering"--Thesis t.p. Vita. Includes bibliographical references.
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Rivest, François. "Knowledge transfer in neural networks : knowledge-based cascade-correlation." Thesis, McGill University, 2002. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=29470.

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Most neural network learning algorithms cannot use knowledge other than what is provided in the training data. Initialized using random weights, they cannot use prior knowledge such as knowledge stored in previously trained networks. This manuscript thesis addresses this problem. It contains a literature review of the relevant static and constructive neural network learning algorithms and of the recent research on transfer of knowledge across neural networks. Manuscript 1 describes a new algorithm, named knowledge-based cascade-correlation (KBCC), which extends the cascade-correlation learning algorithm to allow it to use prior knowledge. This prior knowledge can be provided as, but is not limited to, previously trained neural networks. The manuscript also contains a set of experiments that shows how KBCC is able to reduce its learning time by automatically selecting the appropriate prior knowledge to reuse. Manuscript 2 shows how KBCC speeds up learning on a realistic large problem of vowel recognition.
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Boudani, Nabil I. "Cascade artificial neural networks technique for solving ellipsometry problems." FIU Digital Commons, 1998. http://digitalcommons.fiu.edu/etd/1781.

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Ellipsometry is a well known optical technique used for the characterization of reflective surfaces in study and films between two media. It is based on measuring the change in the state of polarization that occurs as a beam of polarized light is reflected from or transmitted through the film. Measuring this change can be used to calculate parameters of a single layer film such as the thickness and the refractive index. However, extracting these parameters of interest requires significant numerical processing due to the noninvertible equations. Typically, this is done using least squares solving methods which are slow and adversely affected by local minima in the solvable surface. This thesis describes the development and implementation of a new technique using only Artificial Neural Networks (ANN) to calculate thin film parameters. The new method offers a speed in the orders of magnitude faster than preceding methods and convergence to local minima is completely eliminated.
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Senalp, Erdem Turker. "Cascade Modeling Of Nonlinear Systems." Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/3/12608578/index.pdf.

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Modeling of nonlinear systems based on special Hammerstein forms has been considered. In Hammerstein system modeling a static nonlinearity is connected to a dynamic linearity in cascade form. Fundamental contributions of this work are: 1) Introduction of Bezier curve nonlinearity representations
2) Introduction of B-Spline curve nonlinearity representations instead of polynomials in cascade modeling. As a result, local control in nonlinear system modeling is achieved. Thus, unexpected variations of the output can be modeled more closely. As an important demonstration case, a model is developed and named as Middle East Technical University Neural Networks and Cascade Model (METU-NN-C). Application examples are chosen by considering the Near-Earth space processes, which are important for navigation, telecommunication and many other technical applications. It is demonstrated that the models developed based on the contributions of this work are especially more accurate under disturbed conditions, which are quantified by considering Space Weather parameters. Examples include forecasting of Total Electron Content (TEC), and mapping
estimation of joint angle of simple forced pendulum
estimation of joint angles of spring loaded inverted double pendulum with forced table
identification of Van der Pol oscillator
and identification of speakers. The operation performance results of the International Reference Ionosphere (IRI-2001), METU Neural Networks (METU-NN) and METU-NN-C models are compared qualitatively and quantitatively. As a numerical example, in forecasting the TEC by using the METU-NN-C having Bezier curves in nonlinearity representation, the average absolute error is 1.11 TECu. The new cascade models are shown to be promising for system designers and operators.
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Černík, Tomáš. "Neuronové sítě s proměnnou topologií." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255440.

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Master theses deals with Constructive Neural newtorks. First part describes neural networks and coresponding mathematical models. Furher, it shows basic algorithms for learning neural networks and desribes basic constructive algotithms and their modifications. The second part deals with implementation details of selected algorithms and provides their comparision. Further comparision with backpropagation algorithm is provided.
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Kannan, Suresh Kumar. "Adaptive Control of Systems in Cascade with Saturation." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7566.

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This thesis extends the use of neural-network-based model reference adaptive control to systems that occur as cascades. In general, these systems are not feedback linearizable. The approach taken is that of approximate feedback linearization of upper subsystems whilst treating the lower-subsystem states as virtual actuators. Similarly, lower-subsystems are also feedback linearized. Typically, approximate inverses are used for linearization purposes. Model error arising from the use of an approximate inverse is minimized using a neural-network as an adaptive element. Incorrect adaptation due to (virtual) actuator saturation and dynamics is avoided using the Pseudocontrol Hedging method. Using linear approximate inverses and linear reference models generally result in large desired pseudocontrol for large external commands. Even if the provided external command is feasible (null-controllable), there is no guarantee that the reference model trajectory is feasible. In order to mitigate this, nonlinear reference models based on nested-saturation methods are used to constrain the evolution of the reference model and thus the plant states. The method presented in this thesis lends itself to the inner-outer loop control of air vehicles, where the inner-loop controls attitude dynamics and the outer-loop controls the translational dynamics of the vehicle. The outer-loop treats the closed loop attitude dynamics as an actuator. Adaptation to uncertainty in the attitude, as well as the translational dynamics, is introduced, thus minimizing the effects of model error in all six degrees of freedom and leading to more accurate position tracking. A pole-placement approach is used to choose compensator gains for the tracking error dynamics. This alleviates timescale separation requirements, allowing the outer loop bandwidth to be closer to that of the inner loop, thus increasing position tracking performance. A poor model of the attitude dynamics and a basic kinematics model is shown to be sufficient for accurate position tracking. In particular, the inner-outer loop method was used to control an unmanned helicopter and has subsequently been applied to a ducted-fan, a fixed-wing aircraft that transitions in and out of hover, and a full-scale rotorcraft. Experimental flight test results are also provided for a subset of these vehicles.
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Riley, Mike J. W. "Evaluating cascade correlation neural networks for surrogate modelling needs and enhancing the Nimrod/O toolkit for multi-objective optimisation." Thesis, Cranfield University, 2011. http://dspace.lib.cranfield.ac.uk/handle/1826/6796.

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Engineering design often requires the optimisation of multiple objectives, and becomes significantly more difficult and time consuming when the response surfaces are multimodal, rather than unimodal. A surrogate model, also known as a metamodel, can be used to replace expensive computer simulations, accelerating single and multi-objective optimisation and the exploration of new design concepts. The main research focus of this work is to investigate the use of a neural network surrogate model to improve optimisation of multimodal surfaces. Several significant contributions derive from evaluating the Cascade Correlation neural network as the basis of a surrogate model. The contributions to the neural network community ultimately outnumber those to the optimisation community. The effects of training this surrogate on multimodal test functions are explored. The Cascade Correlation neural network is shown to map poorly such response surfaces. A hypothesis for this weakness is formulated and tested. A new subdivision technique is created that addresses this problem; however, this new technique requires excessively large datasets upon which to train. The primary conclusion of this work is that Cascade Correlation neural networks form an unreliable basis for a surrogate model, despite successes reported in the literature. A further contribution of this work is the enhancement of an open source optimisation toolkit, achieved by the first integration of a truly multi-objective optimisation algorithm.
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Gervini, Vitor Irigon. "Modelagem e controle de um servoposicionador pneumático via redes neurais." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/110080.

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Visando apoiar o desenvolvimento de controladores para servoposicionadores pneumáticos, é apresentada no presente trabalho uma proposta de um procedimento baseado no uso de redes neurais para a determinação de modelos matemáticos precisos que possam ser aplicados tanto para a simulação do seu comportamento dinâmico quanto na estrutura de controladores que utilizam estratégias baseadas em modelos. No âmbito do trabalho, esse procedimento foi testado por meio de sua aplicação na identificação das forças de atrito e da relação pressão/vazão mássica nos orifícios de controle da servoválvula (que consistem nas principais não-linearidades envolvidas em tais sistemas). Além disso, determinou-se através de redes neurais a relação inversa entre as vazões desejadas e o sinal de controle da servoválvula (difeomorfismo), a qual é aplicada em técnicas de controle baseadas em modelos. Visando validar o procedimento de modelagem proposto, foram realizadas simulações em malha aberta e malha fechada, cujos resultados são comparados com os de experimentos realizados em uma bancada de testes. Com o intuito de comprovar sua eficácia em aplicações de controle, o modelo baseado em redes neurais foi utilizado no desenvolvimento de um controlador não-linear sintetizado de acordo com uma estratégia em cascata (a qual foi já testada em outros trabalhos, mostrando resultados satisfatórios quando aplicada ao controle de servoposicionadores pneumáticos). No entanto, essa estratégia apresenta dificuldades de implantação em decorrência das dificuldades associadas ao processo de identificação dos parâmetros do sistema, que são especialmente trabalhosos neste caso. As características de estabilidade em malha fechada foram analisadas por meio do segundo método Lyapunov. Os resultados experimentais em malha fechada obtidos atestam a eficácia da estratégia de controle proposta.
The development of a precise positioning system has motivated several researches in the pneumatic systems control area to overcome the problems caused by these nonlinearities, by appropriate feedback control algorithms. In this work it is proposed a methodology based on neural networks to achieve accurate mathematical models that can be used in simulation as in controllers techniques based on models. This methodology was tested through its application in identifying the phenomenon of friction and the relationship pressure/mass flow through servo valve orifices control holes. Furthermore, using neural networks, the inverse relationship between the desired flow rates and control signal of servo valve (diffeomorphism), which is applied in various control techniques based on models, was determined. To evaluate the proposed modeling methodology, simulations were done in open and closed loop, and the results were compared with experiments conducted on a real pneumatic servo positioning system. A neural network based model was used to develop a nonlinear controller according to a cascade strategy with friction compensation (which has been tested on other studies showing satisfactory results when applied to pneumatic servo positioning control). The cascade control strategy, despite showing a good performance in trajectory tracking, presents significant difficulties in implementation due mainly to difficulties associated with the system parameters identification process, which are especially expensive. The characteristics of the closed loop stability were analyzed by Lyapunov method. The experimental results obtained in closed loop attest the efficiency of the proposed control strategy.
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Borges, Fábio Augusto Pires. "Controle em cascata de um atuador hidráulico utilizando redes neurais." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/165587.

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No presente trabalho, é realizada a modelagem e identificação de um serovoposicionador hidráulico de uma bancada de testes. As expressões analíticas tradicionalmente utilizadas em uma estratégia em cascata aplicada ao controle de trajetória de posição são obtidas. A estratégia em questão utiliza, conjuntamente, a linearização por realimentação como lei de controle do subsistema hidráulico e a lei de controle de Slotine e Li no subsistema mecânico. Com base na mesma estratégia, um controlador em cascata neural é proposto. Em tal controlador, a função analítica que representa o mapa inverso, presente na linearização por realimentação, e a função de compensação de atrito utilizada na lei de Slotine e Li são substituídas por funções constituidas por meio de redes neurais de perceptrons de múltiplas camadas. Essas redes neurais têm como entradas os estados do sistema e também a temperatura do fluido hidráulico. O novo controlador é apresentado em uma versão onde as redes neurais são aplicadas sem modificações on-line e em outra, onde são apresentadas leis de controle adaptativo para as mesmas. A prova de estabilidade do sistema em malha fechada é apresentada em ambos os casos. Resultados experimentais do controle de seguimento de trajetórias de posição em diferentes temperaturas do fluido hidráulico são apresentados. Esses resultados demonstram a maior efetividade do controlador proposto em relação aos controladores clássicos PID e PID+feefforward e ao controlador em cascata com funções analíticas fixas. Os experimentos são realizados em duas situações: quando não ocorrem variações paramétricas importantes no sistema, onde é utilizado o controlador em cascata neural fixo e quando ocorrem essas variações, onde se utiliza o controlador em cascata neural adaptativo.
In this work, the modeling and identification of a hydraulic actuator testing setup are performed and the analytical expressions that are used in a cascade control strategy applyied in a position trajectory tracking control are designed. Such cascade strategy uses the feedback linearization control law in the hydraulical subsystem and the Slotine and Li control law in the mechanical one. Based on this cascade strategy, a neural cascade controller is proposed, for which the analytical function used as inversion set in the feedback linearization control law and the friction function compensation of the Slotine and Li control law are replaced by multi layer perceptrons neural networks where the inputs are the states of the system and the hydraulic fluid temperature. The novel controller is introduced in two different aproachs: the first one where the neural networks do not have on-line modifications and the second one where adaptive control laws are proposed. For both of them the stability proof of the closed-loop system is presented. Experimental results about some position tracking controls performed in different fluid temperature are showed. The results show that the novel controller is more efective than the classical PID, PID+feedforward and the traditional analytical cascade controller. The experiments are performed in two different setups: considering the system without importants parametric variations where is applied the non adaptive cascade neural controller and in the presence of parametric variations where is applied the adaptive cascade neural controller.
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Eklund, Anton. "Cascade Mask R-CNN and Keypoint Detection used in Floorplan Parsing." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-415371.

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Parsing floorplans have been a problem in automatic document analysis for long and have up until recent years been approached with algorithmic methods. With the rise of convolutional neural networks (CNN), this problem too has seen an upswing in performance. In this thesis the task is to recover, as accurately as possible, spatial and geometric information from floorplans. This project builds around instance segmentation models like Cascade Mask R-CNN to extract the bulk of information from a floorplan image. To complement the segmentation, a new style of using keypoint-CNN is presented to find precise locations of corners. These are then combined in a post-processing step to give the resulting segmentation. The resulting segmentation scores exceed the current baseline of the CubiCasa5k floorplan dataset with a mean IoU of 72.7% compared to 57.5%. Further, the mean IoU for individual classes is also improved for almost every class. It is also shown that Cascade Mask R-CNN is better suited than Mask R-CNN for this task.
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Books on the topic "Cascade neural networks"

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Puttler, Leon I., Robert A. Zucker, and Hiram E. Fitzgerald. Developmental Science, Alcohol Use Disorders, and the Risk–Resilience Continuum. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190676001.003.0001.

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The origins and expression of addiction are best understood within the context of developmental processes and dynamic systems organization and change. For some individuals, these dynamic processes lead to risk cumulative or cascade effects that embody adverse childhood experiences that exacerbate risk; predict early onset of drinking, smoking, or other substance use; and often lead to a substance use disorder (SUD) during the transitions to adolescence and emergent adulthood. In other cases, protective factors within or outside of the individual’s immediate family enable embodiment of normative stress regulatory systems and neural networks that support resilience and prevention of SUDs. A case study is provided to illustrate these processes and principles of the organization of addictive behavior. Finally, a model of risk to resilience captures the flow of development and the extent to which individual-experience relationships contribute to risk and resilience.
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Book chapters on the topic "Cascade neural networks"

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Senechal, Thibaud, Lionel Prevost, and Shehzad Muhammad Hanif. "Neural Network Cascade for Facial Feature Localization." In Artificial Neural Networks in Pattern Recognition, 141–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12159-3_13.

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Senalp, Erdem Turker, Ersin Tulunay, and Yurdanur Tulunay. "Neural Networks and Cascade Modeling Technique in System Identification." In Artificial Intelligence and Neural Networks, 84–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11803089_10.

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Tong, Bei, Bin Fan, and Fuchao Wu. "Convolutional Neural Networks with Neural Cascade Classifier for Pedestrian Detection." In Communications in Computer and Information Science, 243–57. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-3002-4_21.

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Micheli, Alessio, Diego Sona, and Alessandro Sperduti. "Formal Determination of Context in Contextual Recursive Cascade Correlation Networks." In Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003, 173–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44989-2_22.

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Philpot, David, and Tim Hendtlass. "A Cascade of Neural Networks for Complex Classification." In Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 791. London: CRC Press, 2022. http://dx.doi.org/10.1201/9780429332111-149.

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Moriyasu, Jungo, and Toshimichi Saito. "A Cascade System of Simple Dynamic Binary Neural Networks and Its Sparsification." In Neural Information Processing, 231–38. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12637-1_29.

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Sazadaly, Maxime, Pierre Pinchon, Arthur Fagot, Lionel Prevost, and Myriam Maumy-Bertrand. "Cascade of Ordinal Classification and Local Regression for Audio-Based Affect Estimation." In Artificial Neural Networks in Pattern Recognition, 268–80. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99978-4_21.

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Vamplew, Peter, and Robert Ollington. "On-Line Reinforcement Learning Using Cascade Constructive Neural Networks." In Lecture Notes in Computer Science, 562–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11553939_80.

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Kukla, Elżbieta, and Paweł Nowak. "Facial Emotion Recognition Based on Cascade of Neural Networks." In Advances in Intelligent Systems and Computing, 67–78. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10383-9_7.

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Wen, Yi-Min, and Bao-Liang Lu. "A Cascade Method for Reducing Training Time and the Number of Support Vectors." In Advances in Neural Networks – ISNN 2004, 480–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28647-9_80.

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Conference papers on the topic "Cascade neural networks"

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Tengfei Shen and Dingyun Zhu. "Layered_CasPer: Layered cascade artificial neural networks." In 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252799.

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Ma, Christopher, Xin Dang, Yixin Chen, and Dawn Wilkins. "Pareto cascade modeling of diffusion networks." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489509.

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Kwan and Lee. "Temporal associative memories using cascade and ring architectures." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118305.

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Liu, Chaochao, Wenjun Wang, Pengfei Jiao, Xue Chen, and Yueheng Sun. "Cascade modeling with multihead self-attention." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9207418.

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Wang, Shuainan, Tong Xu, Wei Li, and Haifeng Sun. "CSSD: Cascade Single Shot Face Detector." In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8851713.

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Silva, Eunelson J., Alceu S. Britto, Luiz S. Oliveira, Fabricio Enembreck, Robert Sabourin, and Alessandro L. Koerich. "A two-step cascade classification method." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7965904.

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Yang, J., and V. Honavar. "Experiments with the cascade-correlation algorithm." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170752.

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Charalampidis, Dimitrios, and Chigozie Obiegbu. "Image compression using cascade of neural networks." In AeroSense 2003, edited by Zia-ur Rahman, Robert A. Schowengerdt, and Stephen E. Reichenbach. SPIE, 2003. http://dx.doi.org/10.1117/12.484830.

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Dralus, Grzegorz, and Damian Mazur. "Cascade complex systems — Global modeling using neural networks." In 2016 13th Selected Issues of Electrical Engineering and Electronics (WZEE). IEEE, 2016. http://dx.doi.org/10.1109/wzee.2016.7800198.

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Baig, Mubasher, El-Sayed M. El-Alfy, and Mian M. Awais. "Intrusion detection using a cascade of boosted classifiers (CBC)." In 2014 International Joint Conference on Neural Networks (IJCNN). IEEE, 2014. http://dx.doi.org/10.1109/ijcnn.2014.6889931.

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Reports on the topic "Cascade neural networks"

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Teolis, A., Y. C. Pati, M. C. Peckerar, and S. Shamma. Cascaded Neural-Analog Networks for Real Time Decomposition of Superposed Radar Signals in the Presence of Noise. Fort Belvoir, VA: Defense Technical Information Center, January 1989. http://dx.doi.org/10.21236/ada454381.

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