• No results found

robust feedforward neural network

Automated Species Classification Methods for Passive Acoustic Monitoring of Beaked Whales

Automated Species Classification Methods for Passive Acoustic Monitoring of Beaked Whales

... hand, neural network classifiers trained in a supervised fashion are expected to be more robust to noise, and are capable of classification tasks which are not linearly ...a feedforward ...

45

TRAINING AND DEVELOPMENT OFARTIFICIAL NEURAL NETWORK MODELS: SINGLE LAYER 
FEEDFORWARD AND MULTI LAYER FEEDFORWARD NEURAL NETWORK

TRAINING AND DEVELOPMENT OFARTIFICIAL NEURAL NETWORK MODELS: SINGLE LAYER FEEDFORWARD AND MULTI LAYER FEEDFORWARD NEURAL NETWORK

... Figure 8a and 8b give the BER performance of STBC-OFDM systems in frequency-selective fading channel using BPSK and QPSK modulation respectively, with the second path gain=-8dB and time delay of 16 samples. As seen in ...

10

TRAINING AND DEVELOPMENT OFARTIFICIAL NEURAL NETWORK MODELS: SINGLE LAYER 
FEEDFORWARD AND MULTI LAYER FEEDFORWARD NEURAL NETWORK

TRAINING AND DEVELOPMENT OFARTIFICIAL NEURAL NETWORK MODELS: SINGLE LAYER FEEDFORWARD AND MULTI LAYER FEEDFORWARD NEURAL NETWORK

... Among the low level image features, texture has been shown to be effective and objective in CBIR. A variety of techniques have been developed for extracting texture features, broadly classified into the spatial and ...

9

Behavior Emergence in Autonomous Robot Control by Means of Feedforward and Recurrent Neural Networks

Behavior Emergence in Autonomous Robot Control by Means of Feedforward and Recurrent Neural Networks

... the range of 4.3–4.4 and they differ only in the order of few per cent. It can be seen that the MLP networks per- form slightly better, RBF networks are in the middle, while recurrent networks are a bit worse in terms of ...

6

Subgraph Matching Using Graph Neural Network

Subgraph Matching Using Graph Neural Network

... for validation. In this experiment, label dimension (c) is considered as 1 and state dimension as 2. Termination condition is fixed as mean squared error 0.1. The weights of the networks are initialized randomly from (0, ...

5

Robust Neural Network Classifier

Robust Neural Network Classifier

... Artificial Neural Networks have been successfully used in a number of applications due to their highly advantageous properties like parallel processing of information, capacity to handle non-linearity, quick ...

6

Blind Navigation System using Artificial Intelligence

Blind Navigation System using Artificial Intelligence

... artificial neural networks (SIANN), due to their shared-weight architecture and translation invariance ...convolutional neural network can achieve reasonable performance on hard visual recognition ...

5

Using artificial neural network to monitor and predict induction motor bearing (IMB) failure

Using artificial neural network to monitor and predict induction motor bearing (IMB) failure

... Backpropagation learning algorithm is one of the earliest and the most common method for training multilayer feedforward neural networks. Development of this learning algorithm was one of the main reasons ...

7

A Review of Various Methods of Predicting Cervical Cancer

A Review of Various Methods of Predicting Cervical Cancer

... Thanatip Chankong et al. [23] used PAP Smear for automatic, single cervical cell segmentation and classification. This single cell image was segmented into nuclei and cytoplasm and the background used here is FCM ...

6

Audio Classification on Passing Vehicles with Feedforward Neural Network

Audio Classification on Passing Vehicles with Feedforward Neural Network

... (GA), Neural Network and Bayesian ...Multi-Layer Feedforward Neural Network architecture implemented to classifying the type of vehicle based on acoustic ...

9

Benchmarking of the Key Sample Machine

Benchmarking of the Key Sample Machine

... the neural, by gradient descent, which involves the optimization of a nonlinear SSE surface in a high-dimensional space defined by the network parameters, in subset selection the optimization algorithm ...

85

An End to end Approach to Learning Semantic Frames with Feedforward Neural Network

An End to end Approach to Learning Semantic Frames with Feedforward Neural Network

... We present an end-to-end method for learning verb-specific semantic frames with feedfor- ward neural network (FNN). Previous work- s in this area mainly adopt a multi-step pro- cedure including ...

7

Particle Swarm Optimization Feedforward Neural Network for Hourly Rainfall-runoff Modeling in Bedup Basin, Malaysia

Particle Swarm Optimization Feedforward Neural Network for Hourly Rainfall-runoff Modeling in Bedup Basin, Malaysia

... the network is stuck where there exist another set of synaptic weight for which the cost function is smaller than the local minimum in the weight ...by Neural Network (NN) researcher to overcome this ...

10

Neural Networks

Neural Networks

... a feedforward network, due to cycles, although there are still a good number of algorithms which are occasionally used, including Jordan,time delay networks or ...of feedforward networks, like time ...

8

Survey on Various Types of Noise and Methods for Noise Removal

Survey on Various Types of Noise and Methods for Noise Removal

... value is 0.907 for same i.e. good relation between estimated with observed values. Moreover, keeping in mind that ANNs are require less prior knowledge of the system under study, it is expected that it will be a more ...

7

TRAINING AND DEVELOPMENT OFARTIFICIAL NEURAL NETWORK MODELS: SINGLE LAYER 
FEEDFORWARD AND MULTI LAYER FEEDFORWARD NEURAL NETWORK

TRAINING AND DEVELOPMENT OFARTIFICIAL NEURAL NETWORK MODELS: SINGLE LAYER FEEDFORWARD AND MULTI LAYER FEEDFORWARD NEURAL NETWORK

... Ad-hoc network can be outlined as network with none infrastructure. Route discovery is nice concern in Ad-hoc Network as topology changes dynamically. Several routing algorithmic rule exists in ...

7

TRAINING AND DEVELOPMENT OFARTIFICIAL NEURAL NETWORK MODELS: SINGLE LAYER 
FEEDFORWARD AND MULTI LAYER FEEDFORWARD NEURAL NETWORK

TRAINING AND DEVELOPMENT OFARTIFICIAL NEURAL NETWORK MODELS: SINGLE LAYER FEEDFORWARD AND MULTI LAYER FEEDFORWARD NEURAL NETWORK

... This proposed method enables us to program the channels easily for packet forwarding. The utilization of multiple channels can be potentially optimized, thereby maximizing the network capacity of WMN [21]. ...

8

TRAINING AND DEVELOPMENT OFARTIFICIAL NEURAL NETWORK MODELS: SINGLE LAYER 
FEEDFORWARD AND MULTI LAYER FEEDFORWARD NEURAL NETWORK

TRAINING AND DEVELOPMENT OFARTIFICIAL NEURAL NETWORK MODELS: SINGLE LAYER FEEDFORWARD AND MULTI LAYER FEEDFORWARD NEURAL NETWORK

... 3.2 Data Video Retrieval Scheme In designing the vehicle counting system and measuring vehicle speed, the technology used is a video image processing via cameras mounted on the highway b[r] ...

9

TRAINING AND DEVELOPMENT OFARTIFICIAL NEURAL NETWORK MODELS: SINGLE LAYER 
FEEDFORWARD AND MULTI LAYER FEEDFORWARD NEURAL NETWORK

TRAINING AND DEVELOPMENT OFARTIFICIAL NEURAL NETWORK MODELS: SINGLE LAYER FEEDFORWARD AND MULTI LAYER FEEDFORWARD NEURAL NETWORK

... GSM network is divided into three major systems: the switching system (SS), the base station system (BSS), and the operation and support system ...GSM network elements are shown in below ...

9

TRAINING AND DEVELOPMENT OFARTIFICIAL NEURAL NETWORK MODELS: SINGLE LAYER 
FEEDFORWARD AND MULTI LAYER FEEDFORWARD NEURAL NETWORK

TRAINING AND DEVELOPMENT OFARTIFICIAL NEURAL NETWORK MODELS: SINGLE LAYER FEEDFORWARD AND MULTI LAYER FEEDFORWARD NEURAL NETWORK

... The WAPD model will be represented as a generic model for web applications which can be used for many purposes such as automatic test case generation and automatic code transformation.. [r] ...

12

Show all 10000 documents...

Related subjects