Yoshiyasu Takefuji

Functional-link net computing: theory, system architecture, and functionalities

A system architecture and a network computational approach compatible with the goal of devising a general-purpose artificial neural network computer are described. The functionalities of supervised learning and optimization are illustrated, and cluster analysis and associative recall are briefly mentioned.<<ETX>>

A neural network parallel algorithm for channel assignment problems in cellular radio networks

The channel assignment problem involves not only assigning channels or frequencies to each radio cell. but also satisfying frequency constraints given by a compatibility matrix. The proposed parallel algorithm is based on an artificial neural network composed of nm processing elements for an n-cell-m-frequency problem. The algorithm runs not only on a sequential machine but also on a parallel machine with up to a maximum of nm processors. The algorithm was tested by solving eight benchmark problems where the total number of frequencies varied from 100 to 533. The algorithm found the solutions in nearly constant time with nm processors. The simulation results showed that the algorithm found better solutions than the existing algorithm in one out of eight problems. >

A Near-Optimum Parallel Planarization Algorithm

A near-optimum parallel planarization algorithm is presented. The planarization algorithm, which is designed to embed a graph on a plane, uses a large number of simple processing elements called neurons. The proposed system, composed of an N x N neural network array (where N is the number of vertices), not only generates a near-maximal planar subgraph from a nonplanar graph or a planar graph but also embeds the subgraph on a single plane within 0(1) time. The algorithm can be used in multiple-layer problems such as designing printed circuit boards and routing very-large-scale integration circuits.

An artificial hysteresis binary neuron: a model suppressing the oscillatory behaviors of neural dynamics

A hysteresis binary McCulloch-Pitts neuron model is proposed in order to suppress the complicated oscillatory behaviors of neural dynamics. The artificial hysteresis binary neural network is used for scheduling time-multiplex crossbar switches in order to demonstrate the effects of hysteresis. Time-multiplex crossbar switching systems must control traffic on demand such that packet blocking probability and packet waiting time are minimized. The system using n×n processing elements solves an n×n crossbar-control problem with O(1) time, while the best existing parallel algorithm requires O(n) time. The hysteresis binary neural network maximizes the throughput of packets through a crossbar switch. The solution quality of our system does not degrade with the problem size.

A parallel algorithm for tiling problems

A parallel algorithm for tiling with polyominoes is presented. The tiling problem is to pack polyominoes in a finite checkerboard. The algorithm using lxmxn processing elements requires O(1) time, where l is the number of different kinds of polyominoes on an mxn checkerboard. The algorithm can be used for placement of components or cells in a very large-scale integrated circuit (VLSI) chip, designing and compacting printed circuit boards, and solving a variety of two- or three-dimensional packing problems.

A parallel algorithm for broadcast scheduling problems in packet radio networks

A parallel algorithm based on an artificial neural network model for broadcast scheduling problems in packet radio networks is presented. The algorithm requires n*m processing elements for an n-mode-m-slot radio network problem. The algorithm is verified by simulating 13 different networks. >

An artificial maximum neural network: a winner-take-all neuron model forcing the state of the system in a solution domain

A maximum neuron model is proposed in order to force the state of the system to converge to the solution in neural dynamics. The state of the system is always forced in a solution domain. The artificial maximum neural network is used for the module orientation problem and the bipartite subgraph problem. The usefulness of the maximum neural network is empirically demonstrated by simulating randomly generated massive nstances (examples) in both problems. In randomly generated more than one thousand instances our system always converges to the solution within one hundred iteration steps regardless of the problem size. Our simulation results show the effectiveness of our algorithms and support our claim that one class of NP-complete problems may be solvable in a polynomial time.

Implementing fuzzy rule-based systems on silicon chips

The authors address the implementation of a fuzzy simulator (FSIM) and discuss architectures for a general-purpose VLSI fuzzy-inference processor. The FSIM tool aids in the rapid prototyping of fuzzy production system (FPS), and represents a convenient transitional step in the implementation of an FPS on silicon. They present a brief theoretical review of fuzzy reasoning, introduce the FSIM, and discuss FPS development using the FSIM. An overall picture of the various stages involved in developing a fuzzy-inference processor is provided. The authors then outline the general architecture of a VLSI inference processor for FPSs. To further illustrate the development of a fuzzy inference processor, they describe an FPS example from conceptualization to implementation on silicon chips.<<ETX>>

A parallel improvement algorithm for the bipartite subgraph problem

The authors propose the first parallel improvement algorithm using the maximum neural network model for the bipartite subgraph problem. The goal of this NP-complete problem is to remove the minimum number of edges in a given graph such that the remaining graph is a bipartite graph. A large number of instances have been simulated to verify the proposed algorithm, with the simulation result showing that the algorithm finds a solution within 200 iteration steps and the solution quality is superior to that of the best existing algorithm. The algorithm is extended for the K-partite subgraph problem where no algorithm has been proposed.

Parallel algorithms for finding a near-maximum independent set of a circle graph

A parallel algorithm for finding a near-maximum independent set in a circle graph is presented. An independent set in a graph is a set of vertices, no two of which are adjacent. A maximum independent set is an independent set whose cardinality is the largest among all independent sets of a graph. The algorithm is modified for predicting the secondary structure in ribonucleic acids (RNA). The proposed system, composed of an n neural network array (where n is the number of edges in the circle graph of the number of possible base pairs), not only generates a near-maximum independent set but also predicts the secondary structure of ribonucleic acids within several hundred iteration steps. The simulator discovered several solutions which are more stable structures, in a sequence of 359 bases from the potato spindle tuber viroid, than previously proposed structures.