Computers are now no longer only membranous Humans Used for their jobs, but as already started to operate to replace most of the human work does not Require That thinking and routines. Further development experts are trying to replace the system of the human brain into a computer system.
Neural network is one of the information processing system Designed to mimic the way human brains work in conducting a problem with the process of learning through on their synaptic weight changes. Neural network is Able to identify activities based on past data. Past data will from be studied by artificial neural networks capable That have to inform decisions on That data have not been studied.
Neural network is defined as a system of information processing have That resembling human neural Characteristics. Neural network is created as a mathematical model of understanding generelization Humans (human cognition) based upon assumptions Mutation
1st. Information processing occurs in simple elements Called neurons.
2. Signal Flow Between the nerve cells / neurons via a link connection.
3. Each connection link has a weight Which Will Be Used to double / multiplying sent through the signal.
4. Each nerve cell function will of activation apply to the weighted sum of signals coming to him "to determine the output signal.
Artificial neural networks have a large excess dibandingakan with another calculation method, namely:
1st. The ABILITY even though on their acquired knowledge in a disturbance and uncertain conditions.
2. Ability to present knowledge flexibly.
3. The ABILITY to Provide tolerance to a distortion (error / faults), Nowhere a small disturbance in the data Can be regarded as noise (shake) them.
4. Ability to process knowledge efficiently for wearing a parallel system, so That Time needed to operate Them Is Becoming Shorter.
With a very good level of ABILITY, Some applications of artificial neural network is suitable to apply to:
1st. Classification, selecting one input specific data into one category specified.
2. Association, describes an object as a whole only with a part of another object.
3. Self organizing, ABILITY to process the input data without having to have the data as a target.
4. Optimization, finding an answer or solution best That Can optimizing so often with a cost function (the optimizer).
Characteristic determined by the artificial neural network;
1st. The pattern of relations Between neurons (Called the network architecture)
2. The method to determine the connection weights (Called training or learning network)
3. Activation function.
The basic concept of neural networks
Arsitecture division of neural networks Can be seen from the number of working framework and interconnection schemes. Working framework artificial neural network bias seen from the number of layers and the number of nodes on all layers.
Layer neural networks Compiler Can be divided into three, namely;
1st. Input layer: Node-node in the input layer of input units is Called. Input unit receives input from the outside world. Input is entered is representation of a problem.
2. Hidden layer: Node-node in the hidden layers, hidden units is Called. The output of this layer is not directly observable.
3. Output layer: Node-node at the output layer of output units is Called. Output or the output of this layer is the output of neural networks to a problem.
Most of the neural network adjusts its weight During the weight-training procedure. Training Can be guided training (supervised training) Nowhere pair targets the required inputs for Each pattern was Trained. The second kind is not guided training (unsupervised training). In this method, the adjustment of weights (as a response to the input), the target need not be accompanied. In no supervised training, the network classifies the existing patterns based on category similarity.
The difference is the use of guided training class membership information of Each training example. With this information unsupervised training algorithm for pattern classification cans detect the error as a feedback in the network.
While not supervised neural network training based on how to Modify parameters in a way That masks any sense. In this training model, neural networks do not utilize of membership is no class of training examples, but use the information in a group of neurons to local Modify parameters.
Artificial neural network to solve the problem through a process of learning from examples. Usually the artificial neural network is given a set of training patterns Which parties in a set of sample patterns. From the example neural network learning process.
Can Humans learn, understand, and remember it fully, partially, and Sometimes not all, depending on the person's capacity to learn and store information in on their brains. As the brain stores information is not in full then it is likely to lose the information stored in the brain it will from be great.
The main difference, Between the human brain with an artificial neural network is biased forget That the human brain, whereas neural networks are not likely to forget. Trained neural networks have been Crops Will Be Deeply and Permanently serve targeted information within the cells. Nerve cells in the neural networks Can not be damaged while the human nerve cells is likely corrupted. When nerve cells in the human brain is damaged then the information contained therein will of will of some lost and people forget the information contained therein.
Data and information on human cells is stored in a structured unit in the brain. While the neural network, data and information stored in the weights and biases shape files so That Can be the Anticipation of potential future damage by using a back-up or data backups.
Another difference is the accuracy. When finished Trained neural network, then he Will Be Able to solve the problem of premises The Same Same results even if the problem is repeated Arm-time, while the Humans are not Able to do so.
In The Same unit length of artificial neural networks Quickly Can transmit more information than the human brain. Because this is the work in electronic neural networks while the human brain works chemically.
The Following is a complete comparison Between the capabilities Possessed by the human brain with a CPU:
Neural network is one of the information processing system Designed to mimic the way human brains work in conducting a problem with the process of learning through on their synaptic weight changes. Neural network is Able to identify activities based on past data. Past data will from be studied by artificial neural networks capable That have to inform decisions on That data have not been studied.
Neural network is defined as a system of information processing have That resembling human neural Characteristics. Neural network is created as a mathematical model of understanding generelization Humans (human cognition) based upon assumptions Mutation
1st. Information processing occurs in simple elements Called neurons.
2. Signal Flow Between the nerve cells / neurons via a link connection.
3. Each connection link has a weight Which Will Be Used to double / multiplying sent through the signal.
4. Each nerve cell function will of activation apply to the weighted sum of signals coming to him "to determine the output signal.
Artificial neural networks have a large excess dibandingakan with another calculation method, namely:
1st. The ABILITY even though on their acquired knowledge in a disturbance and uncertain conditions.
2. Ability to present knowledge flexibly.
3. The ABILITY to Provide tolerance to a distortion (error / faults), Nowhere a small disturbance in the data Can be regarded as noise (shake) them.
4. Ability to process knowledge efficiently for wearing a parallel system, so That Time needed to operate Them Is Becoming Shorter.
With a very good level of ABILITY, Some applications of artificial neural network is suitable to apply to:
1st. Classification, selecting one input specific data into one category specified.
2. Association, describes an object as a whole only with a part of another object.
3. Self organizing, ABILITY to process the input data without having to have the data as a target.
4. Optimization, finding an answer or solution best That Can optimizing so often with a cost function (the optimizer).
Characteristic determined by the artificial neural network;
1st. The pattern of relations Between neurons (Called the network architecture)
2. The method to determine the connection weights (Called training or learning network)
3. Activation function.
The basic concept of neural networks
Arsitecture division of neural networks Can be seen from the number of working framework and interconnection schemes. Working framework artificial neural network bias seen from the number of layers and the number of nodes on all layers.
Layer neural networks Compiler Can be divided into three, namely;
1st. Input layer: Node-node in the input layer of input units is Called. Input unit receives input from the outside world. Input is entered is representation of a problem.
2. Hidden layer: Node-node in the hidden layers, hidden units is Called. The output of this layer is not directly observable.
3. Output layer: Node-node at the output layer of output units is Called. Output or the output of this layer is the output of neural networks to a problem.
Most of the neural network adjusts its weight During the weight-training procedure. Training Can be guided training (supervised training) Nowhere pair targets the required inputs for Each pattern was Trained. The second kind is not guided training (unsupervised training). In this method, the adjustment of weights (as a response to the input), the target need not be accompanied. In no supervised training, the network classifies the existing patterns based on category similarity.
The difference is the use of guided training class membership information of Each training example. With this information unsupervised training algorithm for pattern classification cans detect the error as a feedback in the network.
While not supervised neural network training based on how to Modify parameters in a way That masks any sense. In this training model, neural networks do not utilize of membership is no class of training examples, but use the information in a group of neurons to local Modify parameters.
Artificial neural network to solve the problem through a process of learning from examples. Usually the artificial neural network is given a set of training patterns Which parties in a set of sample patterns. From the example neural network learning process.
Can Humans learn, understand, and remember it fully, partially, and Sometimes not all, depending on the person's capacity to learn and store information in on their brains. As the brain stores information is not in full then it is likely to lose the information stored in the brain it will from be great.
The main difference, Between the human brain with an artificial neural network is biased forget That the human brain, whereas neural networks are not likely to forget. Trained neural networks have been Crops Will Be Deeply and Permanently serve targeted information within the cells. Nerve cells in the neural networks Can not be damaged while the human nerve cells is likely corrupted. When nerve cells in the human brain is damaged then the information contained therein will of will of some lost and people forget the information contained therein.
Data and information on human cells is stored in a structured unit in the brain. While the neural network, data and information stored in the weights and biases shape files so That Can be the Anticipation of potential future damage by using a back-up or data backups.
Another difference is the accuracy. When finished Trained neural network, then he Will Be Able to solve the problem of premises The Same Same results even if the problem is repeated Arm-time, while the Humans are not Able to do so.
In The Same unit length of artificial neural networks Quickly Can transmit more information than the human brain. Because this is the work in electronic neural networks while the human brain works chemically.
The Following is a complete comparison Between the capabilities Possessed by the human brain with a CPU:
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