biologically inspired neural networks
Electronic Tongue and Neural Networks, Biologically Inspired Systems Applied to Classifying Coffee Samples
... The information given by electronic tongue instruments is analyzed through multi-variant statistical tech- niques [15] [16]. The selection of the statistical analysis method depends on the type of study being carried out ...
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A Review of Heuristic Global Optimization Based Artificial Neural Network Training Approahes
... A review of the literature shows that optimization algorithms approximate nonlinear functions and provide near accurate solutions. Biologically inspired optimization algorithms like GA, PSO, ACO, ABC, AFSA ...
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A biologically inspired denial of service detector using the random neural network
... applying biologically-inspired methods to achieve some of their self-* ...Random Neural Networks (RNN) which are inspired by the random spiking behaviour of the biological ...
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Biologically inspired intrusion detection (BIID): A review
... on networks. Functionality of networks is being compromised as these attacks have dramatically ...bio- inspired intrusion detection approaches, this paper investigates the Genetic Algorithm (GA), ...
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Biologically inspired emotion recognition from speech
... Artificial neural networks are based on this idea, since they were designed to mimic the biological neural net- works found in the human ...feed-forward neural networks are employed, in ...
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Application Of Hybrid Model For Forecasting Prices Of Jasmine Flower In Bangalore, India
... A neural network is a biologically inspired nonlinear parallel computing paradigm for information processing and exploratory analysis having a distinct ordering among the sets of neurons arranged as ...
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A biologically inspired neural network controller for ballistic arm movements
... Artificial Neural Networks (ANN) because of their capabilities to adapt and to generalise to new ...the neural learning/adaptation processes to their artificial replica, ANN have been used in some ...
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Recovery Management in All Optical Networks Using Biologically-Inspired Complex Adaptive System
... basically inspired by Nature and offers wide range of techniques and analysis tools that are truly motivated from the fields of Biology and ...Artificial Neural Networks and Swarm Intelligence are ...
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Artificial Neural Network Training By Using Regrouping Particle Swarm Optimization
... The concepts of ANNs have been around since the 1950 and are biologically inspired from the view of human brain. The complex relationship between the input variables and output variables is established by ...
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Biologically motivated circuits for third generation neural networks
... Artificial neural networks, more commonly referred to simply as neural networks, are computational devices which are inspired by, and attempt to emulate, the operation of biological ...
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Convolutional Neural Network with Biologically Inspired Retinal Structure
... The state of the art performance results for the CIFAR-10 dataset was achieved by various approaches. Without data augmentation, Deep Supervised Networks (DSN) achieved 90.23% accuracy (Lee, Xie, Gallagher, Zhang, ...
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Biologically inspired vision for human robot interaction
... a biologically inspired vision system for human-robot interaction which integrates several components: visual saliency, stereo vision, face and hand detection and gesture ...
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Refinement of biologically inspired models of reinforcement learning
... Another interpretation of the present results deals with the rate of decay of the eligibility trace (ETP). As previously reviewed (See 1.2.2.7), the eligibility trace refers to the rate at which sensory events are ...
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Clustering of chemical compounds using unsupervised neural networks algorithms : a comparison
... Figure 2 shows the results of all the three methods: the Kohonen SOM (with linear and exponential learning rates), Neural Gas, and Enhanced neural gas in comparison with Wards and group average methods. The ...
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Brain-inspired replay for continual learning with artificial neural networks
... methods are successful for scenarios in which tasks must be learned incrementally, they are unable to incrementally learn new classes. Only another neuroscience-inspired approach, replaying examples representative ...
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Parameter optimization of evolving spiking neural networks using improved firefly algorithm for classification tasks
... SNN are the third generation of neural network model. The model uses spikes as a substitute and analyses the pulse coded information (Gerstner, 2001; Gerstner et al., 1993; Gerstner & van Hemmen, 1994; N. ...
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Prediction of Heart Diseases on the basis of the Cleveland Database
... In [3] Xiaoyong Chai and Li Deng and Qiang Yang and Charles X. Ling used dataset in the experiments and ran a 3- fold cross validation on these data sets. In [4] Gavin Brown shown An ensemble consisting of two ...
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Biologically inspired object categorization in cluttered scenes
... The overall system’s architecture is illustrated in Figure 1. The system consists of three parts working together to classify an input image. The first part is a preprocessing component. In this part, the system extracts ...
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Biologically Inspired Visual Control of Flying Robots
... of biologically inspired visual ...apply biologically plausible techniques to flying robots, and in particular, not restrict myself to strategies which only considered balancing optical flow (often ...
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A new model for classifying DNA code inspired by neural networks and FSA
... Abstract. This paper introduces a new model of classifiers CL(V, E, `, r) designed for classifying DNA sequences and combining the flexibility of neural networks and the generality of finite state automata. ...
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