• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.449-449
• /
• 2000

Many researchers have studied architectures for non-Neumann's computers because of escaping its bottleneck. To avoid the bottleneck, a neuron-based computer has been developed. The computer has only neurons and their connections, which are constructed of the learning. But still it has information processing facilities, and at the same time, it is like as a simplified brain to make inference; it is called "neuron-computer". No instructions are considered in any neural network usually; however, to complete complex processing on restricted computing resources, the processing must be reduced to primitive actions. Therefore, we introduce the instructions to the neuron-computer, in which the most important function is implications. There is an implication represented by binary-operators, but general implications for multi-value or fuzzy logics can't be done. Therefore, we need to use Lukasiewicz' operator at least. We investigated a neuron-computer having instructions for general implications. If we use the computer, the effective inferences base on multi-value logic is executed rapidly in a small logical unit.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.7 no.11
• /
• pp.71-79
• /
• 2017

In this paper, we implemented the exchange rate forecasting neural network using heterogeneous computing. Exchange rate forecasting requires a large amount of data. We used a neural network that could leverage this data accordingly. Neural networks are largely divided into two processes: learning and verification. Learning took advantage of the CPU. For verification, RTL written in Verilog HDL was run on FPGA. The structure of the neural network has four input neurons, four hidden neurons, and one output neuron. The input neurons used the US $ 1, Japanese 100 Yen, EU 1 Euro, and UK £ 1. The input neurons predicted a Canadian dollar value of $ 1. The order of predicting the exchange rate is input, normalization, fixed-point conversion, neural network forward, floating-point conversion, denormalization, and outputting. As a result of forecasting the exchange rate in November 2016, there was an error amount between 0.9 won and 9.13 won. If we increase the number of neurons by adding data other than the exchange rate, it is expected that more precise exchange rate prediction will be possible.

• Journal of Institute of Control, Robotics and Systems
• /
• v.6 no.7
• /
• pp.594-601
• /
• 2000

EIA-709.1 Control Network Protocol is the basic protocol of LonWorks systems that is emerg-ing as a fieldbus device. In this paper the protocol is implemented by using VHDL with FPGA and C program on an Intel 8051 processor. The protocol from the physical layer to the network layer of EIA-709.1 is im-plemented in a hardware level,. So it decreases the load of the CPU for implementing the protocol. We verify the commercial feasibility of the hardware through the communication test with Neuron Chip. based on EIA-709.1 protocol which is used in industrial fields. The developed protocol based on FPGA becomes one of IP can be applicable to various industrial field because it is implemented by VHDL.